publications

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2024

ICASSP
Streaming Active Learning for Regression Problems Using Regression via Classification

Shota Horiguchi, Kota Dohi, and Yohei Kawaguchi

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2024

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One of the challenges in deploying a machine learning model is that the model’s performance degrades as the operating environment changes. To maintain the performance, streaming active learning is used, in which the model is retrained by adding a newly annotated sample to the training dataset if the prediction of the sample is not certain enough. Although many streaming active learning methods have been proposed for classification, few efforts have been made for regression problems, which are often handled in the industrial field. In this paper, we propose to use the regression-via-classification framework for streaming active learning for regression. Regression-via-classification transforms regression problems into classification problems so that streaming active learning methods proposed for classification problems can be applied directly to regression problems. Experimental validation on four real data sets shows that the proposed method can perform regression with higher accuracy at the same annotation cost.
@inproceedings{horiguchi2024streaming, title = {Streaming Active Learning for Regression Problems Using Regression via Classification}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, author = {Horiguchi, Shota and Dohi, Kota and Kawaguchi, Yohei}, year = {2024}, month = apr, pages = {4955--4959}, }

Preprint
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits

Hiroyuki Namba, Shota Horiguchi, Masaki Hamamoto, and Masashi Egi

arXiv:2402.08209, Feb 2024

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Data cleansing aims to improve model performance by removing a set of harmful instances from the training dataset. Data Shapley is a common theoretically guaranteed method to evaluate the contribution of each instance to model performance; however, it requires training on all subsets of the training data, which is computationally expensive. In this paper, we propose an iterative method to fast identify a subset of instances with low data Shapley values by using the thresholding bandit algorithm. We provide a theoretical guarantee that the proposed method can accurately select harmful instances if a sufficiently large number of iterations is conducted. Empirical evaluation using various models and datasets demonstrated that the proposed method efficiently improved the computational speed while maintaining the model performance.
@misc{namba2024thresholding, title = {Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits}, author = {Namba, Hiroyuki and Horiguchi, Shota and Hamamoto, Masaki and Egi, Masashi}, year = {2024}, month = feb, howpublished = {arXiv:2402.08209}, }

2023

TASLP
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors

Shota Horiguchi, Shinji Watanabe, Paola Garcia, Yuki Takashima, and Yohei Kawaguchi

IEEE/ACM Transactions on Audio, Speech, and Language Processing, Jan 2023

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A method to perform offline and online speaker diarization for an unlimited number of speakers is described in this paper. End-to-end neural diarization (EEND) has achieved overlap-aware speaker diarization by formulating it as a multi-label classification problem. It has also been extended for a flexible number of speakers by introducing speaker-wise attractors. However, the output number of speakers of attractor-based EEND is empirically capped; it cannot deal with cases where the number of speakers appearing during inference is higher than that during training because its speaker counting is trained in a fully supervised manner. Our method, EEND-GLA, solves this problem by introducing unsupervised clustering into attractor-based EEND. In the method, the input audio is first divided into short blocks, then attractor-based diarization is performed for each block, and finally the results of each blocks are clustered on the basis of the similarity between locally-calculated attractors. While the number of output speakers is limited within each block, the total number of speakers estimated for the entire input can be higher than the limitation. To use EEND-GLA in an online manner, our method also extends the speaker-tracing buffer, which was originally proposed to enable online inference of conventional EEND. We introduce a block-wise buffer update to make the speaker-tracing buffer compatible with EEND-GLA. Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND. The experimental results demonstrate that EEND-GLA can perform speaker diarization of an unseen number of speakers in both offline and online inferences.
@article{horiguchi2023online, title = {Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, author = {Horiguchi, Shota and Watanabe, Shinji and Garcia, Paola and Takashima, Yuki and Kawaguchi, Yohei}, volume = {31}, year = {2023}, month = jan, pages = {706-720}, }

APSIPA ASC
Synthetic Data Augmentation for ASR with Domain Filtering

Tuan Vu Ho, Shota Horiguchi, Shinji Watanabe, Paola Garcia, and Takashi Sumiyoshi

In Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Nov 2023

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Recent studies have shown that synthetic speech can effectively serve as training data for automatic speech recognition models. Text data for synthetic speech is mostly obtained from in-domain text or generated text using augmentation. However, obtaining large amounts of in-domain text data with diverse lexical contexts is difficult, especially in low-resource scenarios. This paper proposes using text from a large generic-domain source and applying a domain filtering method to choose the relevant text data. This method involves two filtering steps: 1) selecting text based on its semantic similarity to the available in-domain text and 2) diversifying the vocabulary of the selected text using a greedy-search algorithm. Experimental results show that our proposed method outperforms the conventional text augmentation approach, with the relative reduction of word-error-rate ranging from 6% to 25% on the LibriSpeech dataset and 15% on a low-resource Vietnamese dataset.
@inproceedings{ho2023synthetic, title = {Synthetic Data Augmentation for ASR with Domain Filtering}, booktitle = {Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)}, author = {Ho, Tuan Vu and Horiguchi, Shota and Watanabe, Shinji and Garcia, Paola and Sumiyoshi, Takashi}, year = {2023}, month = nov, pages = {1760-1765}, }
INTERSPEECH
Spoofing Attacker Also Benefits from Large-Scale Self-Supervised Models

Aoi Ito* and Shota Horiguchi*

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2023

(*) Equal contribution

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Large-scale pretrained models using self-supervised learning have reportedly improved the performance of speech anti-spoofing. However, the attacker side may also make use of such models. Also, since it is very expensive to train such models from scratch, pretrained models on the Internet are often used, but the attacker and defender may possibly use the same pretrained model. This paper investigates whether the improvement in anti-spoofing with pretrained models holds under the condition that the models are available to attackers. As the attacker, we train a model that enhances spoofed utterances so that the speaker embedding extractor based on the pretrained models cannot distinguish between bona fide and spoofed utterances. Experimental results show that the gains the anti-spoofing models obtained by using the pretrained models almost disappear if the attacker also makes use of the pretrained models.
@inproceedings{ito2023spoofing, title = {Spoofing Attacker Also Benefits from Large-Scale Self-Supervised Models}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, author = {Ito, Aoi and Horiguchi, Shota}, year = {2023}, month = aug, pages = {5346--5350}, }
INTERSPEECH
CAPTDURE: Captioned Sound Dataset of Single Sources

Yuki Okamoto, Kanta Shimonishi, Keisuke Imoto, Kota Dohi, Shota Horiguchi, and Yohei Kawaguchi

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2023

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In conventional studies on environmental sound separation and synthesis using captions, sound datasets consisting of captions for multiple-source sounds were used for model training. However, in the case of we collect the captions for multiple-source sound, we cannot collect the detailed captions for each sound source. Therefore, it is difficult to extract only the single-source target sound by the model-training method using a conventional captioned sound dataset. We constructed a dataset with captions for a single-source sound that can be used in various tasks that involve environmental sounds, such as environmental sound synthesis. Our dataset consists of 1,044 audio samples and 4,902 captions. We also conducted environmental sound extraction experiments using our dataset and evaluated the performance. The experimental results indicate that the captions for a single-source sound are effective in extracting only the single-source target sound from the mixture sound.
@inproceedings{okamoto2023captdure, title = {{CAPTDURE}: Captioned Sound Dataset of Single Sources}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, author = {Okamoto, Yuki and Shimonishi, Kanta and Imoto, Keisuke and Dohi, Kota and Horiguchi, Shota and Kawaguchi, Yohei}, year = {2023}, month = aug, pages = {1683--1687}, }
SLT
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization

Shota Horiguchi, Yuki Takashima, Shinji Watanabe, and Paola García

In IEEE Spoken Language Technology Workshop (SLT), Jan 2023

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Due to the high performance of multi-channel speech processing, we can use the outputs from a multi-channel model as teacher labels when training a single-channel model with knowledge distillation. To the contrary, it is also known that single-channel speech data can benefit multi-channel models by mixing it with multi-channel speech data during training or by using it for model pretraining. This paper focuses on speaker diarization and proposes to conduct the above bi-directional knowledge transfer alternately. We first introduce an end-to-end neural diarization model that can handle both single- and multi-channel inputs. Using this model, we alternately conduct i) knowledge distillation from a multi-channel model to a single-channel model and ii) finetuning from the distilled single-channel model to a multi-channel model. Experimental results on two-speaker data show that the proposed method mutually improved single- and multi-channel speaker diarization performances.
@inproceedings{horiguchi2023mutual, title = {Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization}, booktitle = {IEEE Spoken Language Technology Workshop (SLT)}, author = {Horiguchi, Shota and Takashima, Yuki and Watanabe, Shinji and Garc{\'i}a, Paola}, year = {2023}, month = jan, pages = {620--625}, }

2022

TASLP
Encoder-Decoder Based Attractors for End-to-End Neural Diarization

Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Paola García

IEEE/ACM Transactions on Audio, Speech, and Language Processing, Mar 2022

🏆 Itakura Prize Innovative Young Researcher Award

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This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization, EEND methods are better in terms of speaker overlap handling. However, EEND still has a disadvantage in that it cannot deal with a flexible number of speakers. To remedy this problem, we introduce encoder-decoder-based attractor calculation module (EDA) to EEND. Once frame-wise embeddings are obtained, EDA sequentially generates speaker-wise attractors on the basis of a sequence-to-sequence method using an LSTM encoder-decoder. The attractor generation continues until a stopping condition is satisfied; thus, the number of attractors can be flexible. Diarization results are then estimated as dot products of the attractors and embeddings. The embeddings from speaker overlaps result in larger dot product values with multiple attractors; thus, this method can deal with speaker overlaps. Because the maximum number of output speakers is still limited by the training set, we also propose an iterative inference method to remove this restriction. Further, we propose a method that aligns the estimated diarization results with the results of an external speech activity detector, which enables fair comparison against cascaded approaches. Extensive evaluations on simulated and real datasets show that EEND-EDA outperforms the conventional cascaded approach.
@article{horiguchi2022encoderdecoder, title = {Encoder-Decoder Based Attractors for End-to-End Neural Diarization}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, author = {Horiguchi, Shota and Fujita, Yusuke and Watanabe, Shinji and Xue, Yawen and Garc{\'i}a, Paola}, volume = {30}, year = {2022}, month = mar, pages = {1493--1507}, }

INTERSPEECH
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models

Yuki Takashima, Shota Horiguchi, Shinji Watanabe, Paola Garcia, and Yohei Kawaguchi

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Sep 2022

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In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model. Conventional approaches require extra parameters of the same size as the model for optimization, and it is difficult to apply these approaches to end-to-end ASR models because they have a huge amount of parameters. To solve this problem, we first investigate which parts of end-to-end ASR models contribute to high accuracy in the target domain while preventing catastrophic forgetting. We conduct experiments on incremental domain adaptation from the LibriSpeech dataset to the AMI meeting corpus with two popular end-to-end ASR models and found that adapting only the linear layers of their encoders can prevent catastrophic forgetting. Then, on the basis of this finding, we develop an element-wise parameter selection focused on specific layers to further reduce the number of fine-tuning parameters. Experimental results show that our approach consistently prevents catastrophic forgetting compared to parameter selection from the whole model.
@inproceedings{takashima2022updating, author = {Takashima, Yuki and Horiguchi, Shota and Watanabe, Shinji and Garcia, Paola and Kawaguchi, Yohei}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, title = {Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End {ASR} Models}, year = {2022}, month = sep, pages = {2218--2222}, }
ICML
Rethinking Fano’s Inequality in Ensemble Learning

Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, and Nobuo Nukaga

In International Conference on Machine Learning (ICML), Jul 2022

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We propose a fundamental theory on ensemble learning that evaluates a given ensemble system by a well-grounded set of metrics. Previous studies used a variant of Fano’s inequality of information theory and derived a lower bound of the classification error rate on the basis of the accuracy and diversity of models. We revisit the original Fano’s inequality and argue that the studies did not take into account the information lost when multiple model predictions are combined into a final prediction. To address this issue, we generalize the previous theory to incorporate the information loss. Further, we empirically validate and demonstrate the proposed theory through extensive experiments on actual systems. The theory reveals the strengths and weaknesses of systems on each metric, which will push the theoretical understanding of ensemble learning and give us insights into designing systems.
@inproceedings{morishita2022rethinking, author = {Morishita, Terufumi and Morio, Gaku and Horiguchi, Shota and Ozaki, Hiroaki and Nukaga, Nobuo}, booktitle = {International Conference on Machine Learning (ICML)}, title = {Rethinking {Fano}'s Inequality in Ensemble Learning}, year = {2022}, month = jul, pages = {15976--16016}, }
Odyssey
Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization

Natsuo Yamashita, Shota Horiguchi, and Takeshi Homma

In The Speaker and Language Recognition Workshop (Odyssey), Jun 2022

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This paper investigates a method for simulating natural conversation in the model training of end-to-end neural diarization (EEND). Due to the lack of any annotated real conversational dataset, EEND is usually pretrained on a large-scale simulated conversational dataset first and then adapted to the target real dataset. Simulated datasets play an essential role in the training of EEND, but as yet there has been insufficient investigation into an optimal simulation method. We thus propose a method to simulate natural conversational speech. In contrast to conventional methods, which simply combine the speech of multiple speakers, our method takes turn-taking into account. We define four types of speaker transition and sequentially arrange them to simulate natural conversations. The dataset simulated using our method was found to be statistically similar to the real dataset in terms of the silence and overlap ratios. The experimental results on two-speaker diarization using the CALLHOME and CSJ datasets showed that the simulated dataset contributes to improving the performance of EEND.
@inproceedings{yamashita2022improving, author = {Yamashita, Natsuo and Horiguchi, Shota and Homma, Takeshi}, title = {Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization}, booktitle = {The Speaker and Language Recognition Workshop (Odyssey)}, year = {2022}, month = jun, pages = {133--140}, }
ICASSP
Multi-Channel End-to-End Neural Diarization with Distributed Microphones

Shota Horiguchi, Yuki Takashima, Paola García, Shinji Watanabe, and Yohei Kawaguchi

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022

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Recent progress on end-to-end neural diarization (EEND) has en-abled overlap-aware speaker diarization with a single neural net-work. This paper proposes to enhance EEND by using multi-channel signals from distributed microphones. We replace Transformer en-coders in EEND with two types of encoders that process a multi-channel input: spatio-temporal and co-attention encoders. Both are independent of the number and geometry of microphones and suitable for distributed microphone settings. We also propose a model adaptation method using only single-channel recordings. With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel in-put was given while maintaining comparable performance with a single-channel input. We also showed that the proposed method performed well even when spatial information is inoperative given multi-channel inputs, such as in hybrid meetings in which the utterances of multiple remote participants are played back from the same loudspeaker.
@inproceedings{horiguchi2022multichannel, title = {Multi-Channel End-to-End Neural Diarization with Distributed Microphones}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, author = {Horiguchi, Shota and Takashima, Yuki and Garc{\'i}a, Paola and Watanabe, Shinji and Kawaguchi, Yohei}, year = {2022}, month = may, pages = {7332--7336}, }
ICASSP
Environmental Sound Extraction Using Onomatopoeic Words

Yuki Okamoto, Shota Horiguchi, Masaaki Yamamoto, Keisuke Imoto, and Yohei Kawaguchi

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022

🏆 IEEE SPS Japan Student Conference Paper Award

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An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We propose an environmental-sound-extraction method using onomatopoeic words to specify the target sound to be extracted. By this method, we estimate a time-frequency mask from an input mixture spectrogram and an onomatopoeic word using a U-Net architecture, then extract the corresponding target sound by masking the spectrogram. Experimental results indicate that the proposed method can extract only the target sound corresponding to the onomatopoeic word and performs better than conventional methods that use sound-event classes to specify the target sound.
@inproceedings{okamoto2022environmental, title = {Environmental Sound Extraction Using Onomatopoeic Words}, author = {Okamoto, Yuki and Horiguchi, Shota and Yamamoto, Masaaki and Imoto, Keisuke and Kawaguchi, Yohei}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2022}, month = may, pages = {221--225}, }

2021

ASRU
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors

Shota Horiguchi, Paola García, Shinji Watanabe, Yawen Xue, Yuki Takashima, and Yohei Kawaguchi

In IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Dec 2021

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Attractor-based end-to-end diarization is achieving comparable accuracy to the carefully tuned conventional clustering-based methods on challenging datasets. However, the main drawback is that it cannot deal with the case where the number of speakers is larger than the one observed during training. This is because its speaker counting relies on supervised learning. In this work, we introduce an unsupervised clustering process embedded in the attractor-based end-to-end diarization. We first split a sequence of frame-wise embeddings into short subsequences and then perform attractor-based diarization for each subsequence. Given subsequence-wise diarization results, inter-subsequence speaker correspondence is obtained by unsupervised clustering of the vectors computed from the attractors from all the subsequences. This makes it possible to produce diarization results of a large number of speakers for the whole recording even if the number of output speakers for each subsequence is limited. Experimental results showed that our method could produce accurate diarization results of an unseen number of speakers. Our method achieved 11.84 %, 28.33 %, and 19.49 % on the CALLHOME, DIHARD II, and DIHARD III datasets, respectively, each of which is better than the conventional end-to-end diarization methods.
@inproceedings{horiguchi2021towards, title = {Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors}, booktitle = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, author = {Horiguchi, Shota and Garc{\'i}a, Paola and Watanabe, Shinji and Xue, Yawen and Takashima, Yuki and Kawaguchi, Yohei}, year = {2021}, month = dec, pages = {98--105}, }
INTERSPEECH
Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers

Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Paola Garcia, and Kenji Nagamatsu

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Sep 2021

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We propose a streaming diarization method based on an end-to-end neural diarization (EEND) model, which handles flexible numbers of speakers and overlapping speech. In our previous study, the speaker-tracing buffer (STB) mechanism was proposed to achieve a chunk-wise streaming diarization using a pre-trained EEND model. STB traces the speaker information in previous chunks to map the speakers in a new chunk. However, it only worked with two-speaker recordings. In this paper, we propose an extended STB for flexible numbers of speakers, FLEX-STB. The proposed method uses a zero-padding followed by speaker-tracing, which alleviates the difference in the number of speakers between a buffer and a current chunk. We also examine buffer update strategies to select important frames for tracing multiple speakers. Experiments on CALLHOME and DIHARD II datasets show that the proposed method achieves comparable performance to the offline EEND method with 1-second latency. The results also show that our proposed method outperforms recently proposed chunk-wise diarization methods based on EEND (BW-EDA-EEND).
@inproceedings{xue2021online2, title = {Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers}, author = {Xue, Yawen and Horiguchi, Shota and Fujita, Yusuke and Takashima, Yuki and Watanabe, Shinji and Garcia, Paola and Nagamatsu, Kenji}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, pages = {3116--3120}, year = {2021}, month = sep, }
INTERSPEECH
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization

Yuki Takashima, Yusuke Fujita, Shota Horiguchi, Shinji Watanabe, Paola Garcia, and Kenji Nagamatsu

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Sep 2021

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In this paper, we present a semi-supervised training technique using pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has shown promising performance compared with traditional clustering-based methods, especially in the case of overlapping speech. However, to get a well-tuned model, EEND requires labeled data for all the joint speech activities of every speaker at each time frame in a recording. In this paper, we explore a pseudo-labeling approach that employs unlabeled data. First, we propose an iterative pseudo-label method for EEND, which trains the model using unlabeled data of a target condition. Then, we also propose a committee-based training method to improve the performance of EEND. To evaluate our proposed method, we conduct the experiments of model adaptation using labeled and unlabeled data. Experimental results on the CALLHOME dataset show that our proposed pseudo-label achieved a 37.4% relative diarization error rate reduction compared to a seed model. Moreover, we analyzed the results of semi-supervised adaptation with pseudo-labeling. We also show the effectiveness of our approach on the third DIHARD dataset.
@inproceedings{takashima2021semisupervised, title = {Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization}, author = {Takashima, Yuki and Fujita, Yusuke and Horiguchi, Shota and Watanabe, Shinji and Garcia, Paola and Nagamatsu, Kenji}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, pages = {3096--3110}, year = {2021}, month = sep, }
ICASSP
End-to-End Speaker Diarization as Post-Processing

Shota Horiguchi, Paola García, Yusuke Fujita, Shinji Watanabe, and Kenji Nagamatsu

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2021

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This paper investigates the utilization of an end-to-end diarization model as post-processing of conventional clustering-based diarization. Clustering-based diarization methods partition frames into clusters of the number of speakers; thus, they typically cannot handle overlapping speech because each frame is assigned to one speaker. On the other hand, some end-to-end diarization methods can handle overlapping speech by treating the problem as multi-label classification. Although some methods can treat a flexible number of speakers, they do not perform well when the number of speakers is large. To compensate for each other’s weakness, we propose to use a two-speaker end-to-end diarization method as post-processing of the results obtained by a clustering-based method. We iteratively select two speakers from the results and update the results of the two speakers to improve the overlapped region. Experimental results show that the proposed algorithm consistently improved the performance of the state-of-the-art methods across CALLHOME, AMI, and DIHARD II datasets.
@inproceedings{horiguchi2021endtoend, title = {End-to-End Speaker Diarization as Post-Processing}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, author = {Horiguchi, Shota and Garc{\'i}a, Paola and Fujita, Yusuke and Watanabe, Shinji and Nagamatsu, Kenji}, year = {2021}, month = may, pages = {7188--7192}, }
SLT
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection

Yuki Takashima, Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Paola Garcia, and Kenji Nagamatsu

In IEEE Spoken Language Technology Workshop (SLT), Jan 2021

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In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND). The EEND system has shown promising performance compared with traditional clustering-based methods, especially in the case of overlapping speech. In this paper, to further improve the performance of the EEND system, we propose a novel multitask learning framework that solves speaker diarization and a desired subtask while explicitly considering the task dependency. We optimize speaker diarization conditioned on speech activity and overlap detection that are subtasks of speaker diarization, based on the probabilistic chain rule. Experimental results show that our proposed method can leverage a subtask to effectively model speaker diarization, and outperforms conventional EEND systems in terms of diarization error rate.
@inproceedings{takashima2021endtoend, title = {End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection}, author = {Takashima, Yuki and Fujita, Yusuke and Watanabe, Shinji and Horiguchi, Shota and Garcia, Paola and Nagamatsu, Kenji}, booktitle = {IEEE Spoken Language Technology Workshop (SLT)}, pages = {849--856}, year = {2021}, month = jan, }
SLT
Online End-to-End Neural Diarization with Speaker-Tracing Buffer

Yawen Xue, Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Paola Garcia, and Kenji Nagamatsu

In IEEE Spoken Language Technology Workshop (SLT), Jan 2021

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This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a speaker’s permutation problem due to the possibility to assign speaker regions incorrectly across the recording. To circumvent this inconsistency, we proposed a speaker-tracing buffer mechanism that selects several input frames representing the speaker permutation information from previous chunks and stores them in a buffer. These buffered frames are stacked with the input frames in the current chunk and fed into a self-attention network. Our method ensures consistent diarization outputs across the buffer and the current chunk by checking the correlation between their corresponding outputs. Additionally, we trained SA-EEND with variable chunk-sizes to mitigate the mismatch between training and inference introduced by the speaker-tracing buffer mechanism. Experimental results, including online SA-EEND and variable chunk-size, achieved DERs of 12.54% for CALLHOME and 20.77% for CSJ with 1.4 s actual latency.
@inproceedings{xue2021online, title = {Online End-to-End Neural Diarization with Speaker-Tracing Buffer}, author = {Xue, Yawen and Horiguchi, Shota and Fujita, Yusuke and Watanabe, Shinji and Garcia, Paola and Nagamatsu, Kenji}, booktitle = {IEEE Spoken Language Technology Workshop (SLT)}, pages = {841--848}, year = {2021}, month = jan, }
SLT
Block-Online Guided Source Separation

Shota Horiguchi, Yusuke Fujita, and Kenji Nagamatsu

In IEEE Spoken Language Technology Workshop (SLT), Jan 2021

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We propose a block-online algorithm of guided source separation (GSS). GSS is a speech separation method that uses diarization information to update parameters of the generative model of observation signals. Previous studies have shown that GSS performs well in multi-talker scenarios. However, it requires a large amount of calculation time, which is an obstacle to the deployment of online applications. It is also a problem that the offline GSS is an utterance-wise algorithm so that it produces latency according to the length of the utterance. With the proposed algorithm, block-wise input samples and corresponding time annotations are concatenated with those in the preceding context and used to update the parameters. Using the context enables the algorithm to estimate time-frequency masks accurately only from one iteration of optimization for each block, and its latency does not depend on the utterance length but predetermined block length. It also reduces calculation cost by updating only the parameters of active speakers in each block and its context. Evaluation on the CHiME-6 corpus and a meeting corpus showed that the proposed algorithm achieved almost the same performance as the conventional offline GSS algorithm but with 32x faster calculation, which is sufficient for real-time applications.
@inproceedings{horiguchi2021blockonline, title = {Block-Online Guided Source Separation}, booktitle = {IEEE Spoken Language Technology Workshop (SLT)}, author = {Horiguchi, Shota and Fujita, Yusuke and Nagamatsu, Kenji}, year = {2021}, month = jan, pages = {236--242}, }

DIHARD
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-vector Clustering Systems Combined by DOVER-Lap

Shota Horiguchi, Nelson Yalta, Paola Garcia, Yuki Takashima, Yawen Xue, Desh Raj, Zili Huang, Yusuke Fujita, Shinji Watanabe, and Sanjeev Khudanpur

In The Third DIHARD Speech Diarization Challenge (DIHARD III), Jan 2021

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This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of the five subsystems: two x-vector-based subsystems, two end-to-end neural diarization-based subsystems, and one hybrid subsystem. We refine each system and all five subsystems become competitive and complementary. After the DOVER-Lap based system combination, it achieved diarization error rates of 11.58 % and 14.09 % in Track 1 full and core, and 16.94 % and 20.01 % in Track 2 full and core, respectively. With their results, we won second place in all the tasks of the challenge.
@inproceedings{horiguchi2022hitachijhu, title = {The {Hitachi-JHU} {DIHARD} {III} System: Competitive End-to-End Neural Diarization and X-vector Clustering Systems Combined by {DOVER-Lap}}, author = {Horiguchi, Shota and Yalta, Nelson and Garcia, Paola and Takashima, Yuki and Xue, Yawen and Raj, Desh and Huang, Zili and Fujita, Yusuke and Watanabe, Shinji and Khudanpur, Sanjeev}, booktitle = {The Third DIHARD Speech Diarization Challenge (DIHARD III)}, year = {2021}, month = jan, }

2020

TPAMI
Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features

Shota Horiguchi, Daiki Ikami, and Kiyoharu Aizawa

IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2020

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End-to-end distance metric learning (DML) has been applied to obtain features useful in many computer vision tasks. However, these DML studies have not provided equitable comparisons between features extracted from DML-based networks and softmax-based networks. In this paper, we present objective comparisons between these two approaches under the same network architecture.
@article{horiguchi2020significance, title = {Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, author = {Horiguchi, Shota and Ikami, Daiki and Aizawa, Kiyoharu}, volume = {42}, number = {5}, year = {2020}, month = may, pages = {1279--1285}, }

INTERSPEECH
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones

Shota Horiguchi, Yusuke Fujita, and Kenji Nagamatsu

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Oct 2020

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A novel framework for meeting transcription using asynchronous microphones is proposed in this paper. It consists of audio synchronization, speaker diarization, utterance-wise speech enhancement using guided source separation, automatic speech recognition, and duplication reduction. Doing speaker diarization before speech enhancement enables the system to deal with overlapped speech without considering sampling frequency mismatch between microphones. Evaluation on our real meeting datasets showed that our framework achieved a character error rate (CER) of 28.7% by using 11 distributed microphones, while a monaural microphone placed on the center of the table had a CER of 38.2%. We also showed that our framework achieved CER of 21.8%, which is only 2.1 percentage points higher than the CER in headset microphone-based transcription.
@inproceedings{horiguchi2020utterancewise, title = {Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, author = {Horiguchi, Shota and Fujita, Yusuke and Nagamatsu, Kenji}, pages = {344--348}, year = {2020}, month = oct, }
INTERSPEECH
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors

Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, and Kenji Nagamatsu

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Oct 2020

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End-to-end speaker diarization for an unknown number of speakers is addressed in this paper. Recently proposed end-to-end speaker diarization outperformed conventional clustering-based speaker diarization, but it has one drawback: it is less flexible in terms of the number of speakers. This paper proposes a method for encoder-decoder based attractor calculation (EDA), which first generates a flexible number of attractors from a speech embedding sequence. Then, the generated multiple attractors are multiplied by the speech embedding sequence to produce the same number of speaker activities. The speech embedding sequence is extracted using the conventional self-attentive end-to-end neural speaker diarization (SA-EEND) network. In a two-speaker condition, our method achieved a 2.69% diarization error rate (DER) on simulated mixtures and a 8.07% DER on the two-speaker subset of CALLHOME, while vanilla SA-EEND attained 4.56% and 9.54%, respectively. In unknown numbers of speakers conditions, our method attained a 15.29% DER on CALLHOME, while the x-vector-based clustering method achieved a 19.43% DER.
@inproceedings{horiguchi2020endtoend, title = {End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, author = {Horiguchi, Shota and Fujita, Yusuke and Watanabe, Shinji and Xue, Yawen and Nagamatsu, Kenji}, pages = {269--273}, year = {2020}, month = oct, }
ICRA
Anticipating the Start of User Interaction for Service Robot in the Wild

Koichiro Ito, Quan Kong, Shota Horiguchi, Takashi Sumiyoshi, and Kenji Nagamatsu

In IEEE International Conference on Robotics and Automation (ICRA), Jun 2020

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A service robot is expected to provide proactive service for visitors who require its help. In contrast to passive service, e.g., providing service only after being spoken to, proactive service initiates an interaction at an early stage, e.g., talking to potential visitors who need the robot’s help in advance. This paper addresses how to anticipate the start of user interaction. We propose an approach using only a single RGB camera that anticipates whether a visitor will come to the robot for interaction or just pass it by. In the proposed approach, we (i) utilize the visitor’s pose information from captured images incorporating facial information, (ii) train a CNN-LSTM–based model in an end-to-end manner with an exponential loss for early anticipation, and (iii) during the training, the network branch for facial keypoints acquired as the part of the human pose information is taught to mimic the branch trained with the face image from a specialized face detector with a human verification. By virtue of (iii), at the inference, we can run our model in an embedded system processing only the pose information without an additional face detector and typical accuracy drop.We evaluated the proposed approach on our collected real world data with a real service robot and publicly available JPL interaction dataset and found that it achieved accurate anticipation performance.
@inproceedings{ito2020anticipating, title = {Anticipating the Start of User Interaction for Service Robot in the Wild}, author = {Ito, Koichiro and Kong, Quan and Horiguchi, Shota and Sumiyoshi, Takashi and Nagamatsu, Kenji}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, year = {2020}, month = jun, pages = {9687--9693}, }

SemEval
Hitachi at SemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition

Terufumi Morishita*, Gaku Morio*, Shota Horiguchi, Hiroaki Ozaki, and Toshinori Miyoshi

In The Forteenth Workshop on Semantic Evaluation (SemEval), Dec 2020

(*) Equal contribution

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Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them. SemEval-2020 task 8, Memotion Analysis, aims at automatically recognizing these emotions of so-called internet memes. In this paper, we propose a simple but effective Modality Ensemble that incorporates visual and textual deep-learning models, which are independently trained, rather than providing a single multi-modal joint network. To this end, we first fine-tune four pre-trained visual models (i.e., Inception-ResNet, PolyNet, SENet, and PNASNet) and four textual models (i.e., BERT, GPT-2, Transformer-XL, and XLNet). Then, we fuse their predictions with ensemble methods to effectively capture cross-modal correlations. The experiments performed on dev-set show that both visual and textual features aided each other, especially in subtask-C, and consequently, our system ranked 2nd on subtask-C.
@inproceedings{morishita2020hitachi, title = {Hitachi at {SemEval-2020} Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition}, author = {Morishita, Terufumi and Morio, Gaku and Horiguchi, Shota and Ozaki, Hiroaki and Miyoshi, Toshinori}, booktitle = {The Forteenth Workshop on Semantic Evaluation (SemEval)}, year = {2020}, month = dec, pages = {1126–-1134}, }
CHiME
CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings

Shinji Watanabe, Michael Mandel, Jon Barker, Emmanuel Vincent, Ashish Arora, Xuankai Chang, Sanjeev Khudanpur, Vimal Manohar, Daniel Povey, Desh Raj, David Snyder, Aswin Shanmugam Subramanian, Jan Trmal, Bar Ben Yair, Christoph Boeddeker, Zhaoheng Ni, Yusuke Fujita, Shota Horiguchi, Naoyuki Kanda, Takuya Yoshioka, and Neville Ryant

In The 6th International Workshop on Speech Processing in Everyday Environments (CHiME-2020), May 2020

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Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge revisits the previous CHiME-5 challenge and further considers the problem of distant multi-microphone conversational speech diarization and recognition in everyday home environments. Speech material is the same as the previous CHiME-5 recordings except for accurate array synchronization. The material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech. This paper provides a baseline description of the CHiME-6 challenge for both segmented multispeaker speech recognition (Track 1) and unsegmented multispeaker speech recognition (Track 2). Of note, Track 2 is the first challenge activity in the community to tackle an unsegmented multispeaker speech recognition scenario with a complete set of reproducible open source baselines providing speech enhancement, speaker diarization, and speech recognition modules.
@inproceedings{watanabe2020chime6, title = {{CHiME-6} {Challenge}: Tackling Multispeaker Speech Recognition for Unsegmented Recordings}, author = {Watanabe, Shinji and Mandel, Michael and Barker, Jon and Vincent, Emmanuel and Arora, Ashish and Chang, Xuankai and Khudanpur, Sanjeev and Manohar, Vimal and Povey, Daniel and Raj, Desh and Snyder, David and Subramanian, Aswin Shanmugam and Trmal, Jan and Yair, Bar Ben and Boeddeker, Christoph and Ni, Zhaoheng and Fujita, Yusuke and Horiguchi, Shota and Kanda, Naoyuki and Yoshioka, Takuya and Ryant, Neville}, booktitle = {The 6th International Workshop on Speech Processing in Everyday Environments (CHiME-2020)}, year = {2020}, month = may, pages = {1--7}, }

Preprint
Neural Speaker Diarization with Speaker-Wise Chain Rule

Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Yawen Xue, Jing Shi, and Nagamatsu Kenji

arXiv:2006.01796, Jun 2020

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Speaker diarization is an essential step for processing multi-speaker audio. Although an end-to-end neural diarization (EEND) method achieved state-of-the-art performance, it is limited to a fixed number of speakers. In this paper, we solve this fixed number of speaker issue by a novel speaker-wise conditional inference method based on the probabilistic chain rule. In the proposed method, each speaker’s speech activity is regarded as a single random variable, and is estimated sequentially conditioned on previously estimated other speakers’ speech activities. Similar to other sequence-to-sequence models, the proposed method produces a variable number of speakers with a stop sequence condition. We evaluated the proposed method on multi-speaker audio recordings of a variable number of speakers. Experimental results show that the proposed method can correctly produce diarization results with a variable number of speakers and outperforms the state-of-the-art end-to-end speaker diarization methods in terms of diarization error rate.
@misc{fujita2020neural, title = {Neural Speaker Diarization with Speaker-Wise Chain Rule}, author = {Fujita, Yusuke and Watanabe, Shinji and Horiguchi, Shota and Xue, Yawen and Shi, Jing and Kenji, Nagamatsu}, year = {2020}, month = jun, howpublished = {arXiv:2006.01796}, }
Preprint
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-Label Classification

Yusuke Fujita, Shinji Watanabe, Shota Horiguchi, Yawen Xue, and Nagamatsu Kenji

arXiv:2003.20966, Feb 2020

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The most common approach to speaker diarization is clustering of speaker embeddings. However, the clustering-based approach has a number of problems; i.e., (i) it is not optimized to minimize diarization errors directly, (ii) it cannot handle speaker overlaps correctly, and (iii) it has trouble adapting their speaker embedding models to real audio recordings with speaker overlaps. To solve these problems, we propose the End-to-End Neural Diarization (EEND), in which a neural network directly outputs speaker diarization results given a multi-speaker recording. To realize such an end-to-end model, we formulate the speaker diarization problem as a multi-label classification problem and introduce a permutation-free objective function to directly minimize diarization errors. Besides its end-to-end simplicity, the EEND method can explicitly handle speaker overlaps during training and inference. Just by feeding multi-speaker recordings with corresponding speaker segment labels, our model can be easily adapted to real conversations. We evaluated our method on simulated speech mixtures and real conversation datasets. The results showed that the EEND method outperformed the state-of-the-art x-vector clustering-based method, while it correctly handled speaker overlaps. We explored the neural network architecture for the EEND method, and found that the self-attention-based neural network was the key to achieving excellent performance. In contrast to conditioning the network only on its previous and next hidden states, as is done using bidirectional long short-term memory (BLSTM), self-attention is directly conditioned on all the frames. By visualizing the attention weights, we show that self-attention captures global speaker characteristics in addition to local speech activity dynamics, making it especially suitable for dealing with the speaker diarization problem.
@misc{fujita2020endtoend, title = {End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-Label Classification}, author = {Fujita, Yusuke and Watanabe, Shinji and Horiguchi, Shota and Xue, Yawen and Kenji, Nagamatsu}, year = {2020}, month = feb, howpublished = {arXiv:2003.20966}, }

2019

ASRU
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models

Naoyuki Kanda, Shota Horiguchi, Yusuke Fujita, Yawen Xue, Kenji Nagamatsu, and Shinji Watanabe

In IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Dec 2019

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This paper investigates the use of target-speaker automatic speech recognition (TS-ASR) for simultaneous speech recognition and speaker diarization of single-channel dialogue recordings. TS-ASR is a technique to automatically extract and recognize only the speech of a target speaker given a short sample utterance of that speaker. One obvious drawback of TS-ASR is that it cannot be used when the speakers in the recordings are unknown because it requires a sample of the target speakers in advance of decoding. To remove this limitation, we propose an iterative method, in which (i) the estimation of speaker embeddings and (ii) TS-ASR based on the estimated speaker embeddings are alternately executed. We evaluated the proposed method by using very challenging dialogue recordings in which the speaker overlap ratio was over 20%. We confirmed that the proposed method significantly reduced both the word error rate (WER) and diarization error rate (DER). Our proposed method combined with i-vector speaker embeddings ultimately achieved a WER that differed by only 2.1 % from that of TS-ASR given oracle speaker embeddings. Furthermore, our method can solve speaker diarization simultaneously as a by-product and achieved better DER than that of the conventional clustering-based speaker diarization method based on i-vector.
@inproceedings{kanda2019simultaneous, title = {Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models}, author = {Kanda, Naoyuki and Horiguchi, Shota and Fujita, Yusuke and Xue, Yawen and Nagamatsu, Kenji and Watanabe, Shinji}, booktitle = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, pages = {31--38}, year = {2019}, month = dec, }
ASRU
End-to-End Neural Speaker Diarization with Self-Attention

Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi, Yawen Xue, Kenji Nagamatsu, and Shinji Watanabe

In IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Dec 2019

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Speaker diarization has been mainly developed based on the clustering of speaker embeddings. However, the clustering-based approach has two major problems; i.e., (i) it is not optimized to minimize diarization errors directly, and (ii) it cannot handle speaker overlaps correctly. To solve these problems, the End-to-End Neural Diarization (EEND), in which a bidirectional long short-term memory (BLSTM) network directly outputs speaker diarization results given a multi-talker recording, was recently proposed. In this study, we enhance EEND by introducing self-attention blocks instead of BLSTM blocks. In contrast to BLSTM, which is conditioned only on its previous and next hidden states, self-attention is directly conditioned on all the other frames, making it much suitable for dealing with the speaker diarization problem. We evaluated our proposed method on simulated mixtures, real telephone calls, and real dialogue recordings. The experimental results revealed that the self-attention was the key to achieving good performance and that our proposed method performed significantly better than the conventional BLSTM-based method. Our method was even better than that of the state-of-the-art x-vector clustering-based method. Finally, by visualizing the latent representation, we show that the self-attention can capture global speaker characteristics in addition to local speech activity dynamics. Our source code is available online at https://github.com/hitachi-speech/EEND.
@inproceedings{fujita2019endtoend, title = {End-to-End Neural Speaker Diarization with Self-Attention}, author = {Fujita, Yusuke and Kanda, Naoyuki and Horiguchi, Shota and Xue, Yawen and Nagamatsu, Kenji and Watanabe, Shinji}, booktitle = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, pages = {296--303}, year = {2019}, month = dec, }
INTERSPEECH
End-to-End Neural Speaker Diarization with Permutation-Free Objectives

Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi, Kenji Nagamatsu, and Shinji Watanabe

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Sep 2019

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In this paper, we propose a novel end-to-end neural-network-based speaker diarization method. Unlike most existing methods, our proposed method does not have separate modules for extraction and clustering of speaker representations. Instead, our model has a single neural network that directly outputs speaker diarization results. To realize such a model, we formulate the speaker diarization problem as a multi-label classification problem, and introduces a permutation-free objective function to directly minimize diarization errors without being suffered from the speaker-label permutation problem. Besides its end-to-end simplicity, the proposed method also benefits from being able to explicitly handle overlapping speech during training and inference. Because of the benefit, our model can be easily trained/adapted with real-recorded multi-speaker conversations just by feeding the corresponding multi-speaker segment labels. We evaluated the proposed method on simulated speech mixtures. The proposed method achieved diarization error rate of 12.28%, while a conventional clustering-based system produced diarization error rate of 28.77%. Furthermore, the domain adaptation with real-recorded speech provided 25.6% relative improvement on the CALLHOME dataset.
@inproceedings{fujita2019endtoend2, title = {End-to-End Neural Speaker Diarization with Permutation-Free Objectives}, author = {Fujita, Yusuke and Kanda, Naoyuki and Horiguchi, Shota and Nagamatsu, Kenji and Watanabe, Shinji}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, year = {2019}, month = sep, pages = {4300--4304}, }
INTERSPEECH
Multimodal Response Obligation Detection with Unsupervised Online Domain Adaptation

Shota Horiguchi, Naoyuki Kanda, and Kenji Nagamatsu

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Sep 2019

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Response obligation detection, which determines whether a dialogue robot has to respond to a detected utterance, is an important function for intelligent dialogue robots. Some studies have tackled this problem; however, they narrow their applicability by impractical assumptions or use of scenario-specific features. Some attempts have been made to widen the applicability by avoiding the use of text modality, which is said to be highly domain dependent, but it decreases the detection accuracy. In this paper, we propose a novel multimodal response obligation detector, which uses visual, audio, and text information for highly-accurate detection, with its unsupervised online domain adaptation to solve the domain dependency problem. Our domain adaptation consists of the weights adaptation of the logistic regression for every modality and an embedding assignment for new words to cope with the high domain dependency of text modality. Experimental results on the dataset collected at a station and commercial building showed that our method achieved high response obligation detection accuracy and was able to handle domain change automatically.
@inproceedings{horiguchi2019multimodal, title = {Multimodal Response Obligation Detection with Unsupervised Online Domain Adaptation}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, author = {Horiguchi, Shota and Kanda, Naoyuki and Nagamatsu, Kenji}, pages = {4180--4184}, year = {2019}, month = sep, }
INTERSPEECH
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition

Naoyuki Kanda, Shota Horiguchi, Ryoichi Takashima, Yusuke Fujita, Kenji Nagamatsu, and Shinji Watanabe

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Sep 2019

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In this paper, we propose a novel auxiliary loss function for target-speaker automatic speech recognition (ASR). Our method automatically extracts and transcribes target speaker’s utterances from a monaural mixture of multiple speakers speech given a short sample of the target speaker. The proposed auxiliary loss function attempts to additionally maximize interference speaker ASR accuracy during training. This will regularize the network to achieve a better representation for speaker separation, thus achieving better accuracy on the target-speaker ASR. We evaluated our proposed method using two-speaker-mixed speech in various signal-to-interference-ratio conditions. We first built a strong target-speaker ASR baseline based on the state-of-the-art lattice-free maximum mutual information. This baseline achieved a word error rate (WER) of 18.06% on the test set while a normal ASR trained with clean data produced a completely corrupted result (WER of 84.71%). Then, our proposed loss further reduced the WER by 6.6% relative to this strong baseline, achieving a WER of 16.87%. In addition to the accuracy improvement, we also showed that the auxiliary output branch for the proposed loss can even be used for a secondary ASR for interference speakers’ speech.
@inproceedings{kanda2019auxiliary, title = {Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition}, author = {Kanda, Naoyuki and Horiguchi, Shota and Takashima, Ryoichi and Fujita, Yusuke and Nagamatsu, Kenji and Watanabe, Shinji}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, year = {2019}, month = sep, pages = {236--240}, }
INTERSPEECH
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party Scenario

Naoyuki Kanda, Christoph Boeddeker, Jens Heitkaemper, Yusuke Fujita, Shota Horiguchi, Kenji Nagamatsu, and Reinhold Haeb-Umbach

In The Annual Conference of the International Speech Communication Association (INTERSPEECH), Sep 2019

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In this paper, we present Hitachi and Paderborn University’s joint effort for automatic speech recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for dinner party recordings obtained by multiple microphone arrays are (1) heavy speech overlaps, (2) severe noise and reverberation, (3) very natural conversational content, and possibly (4) insufficient training data. As an example of a dinner party scenario, we have chosen the data presented during the CHiME-5 speech recognition challenge, where the baseline ASR had a 73.3% word error rate (WER), and even the best performing system at the CHiME-5 challenge had a 46.1% WER. We extensively investigated a combination of the guided source separation-based speech enhancement technique and an already proposed strong ASR backend and found that a tight combination of these techniques provided substantial accuracy improvements. Our final system achieved WERs of 39.94% and 41.64% for the development and evaluation data, respectively, both of which are the best published results for the dataset. We also investigated with additional training data on the official small data in the CHiME-5 corpus to assess the intrinsic difficulty of this ASR task.
@inproceedings{kanda2019guided, title = {Guided Source Separation Meets a Strong {ASR} Backend: {Hitachi/Paderborn University} Joint Investigation for Dinner Party Scenario}, author = {Kanda, Naoyuki and Boeddeker, Christoph and Heitkaemper, Jens and Fujita, Yusuke and Horiguchi, Shota and Nagamatsu, Kenji and Haeb-Umbach, Reinhold}, booktitle = {The Annual Conference of the International Speech Communication Association (INTERSPEECH)}, year = {2019}, month = sep, pages = {1248--1252}, }
ICASSP
Acoustic Modeling for Distant Multi-Talker Speech Recognition with Single- and Multi-Channel Branches

Naoyuki Kanda, Yusuke Fujita, Shota Horiguchi, Rintaro Ikeshita, Kenji Nagamatsu, and Shinji Watanabe

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019

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This paper presents a novel heterogeneous-input multi-channel acoustic model (AM) that has both single-channel and multi-channel input branches. In our proposed training pipeline, a single-channel AM is trained first, then a multi-channel AM is trained starting from the single-channel AM with a randomly initialized multi-channel input branch. Our model uniquely uses the power of a complemen-tal speech enhancement (SE) module while exploiting the power of jointly trained AM and SE architecture. Our method was the foundation for the Hitachi/JHU CHiME-5 system that achieved the second-best result in the CHiME-5 competition, and this paper details various investigation results that we were not able to present during the competition period. We also evaluated and reconfirmed our method’s effectiveness with the AMI Meeting Corpus. Our AM achieved a 30.12% word error rate (WER) for the development set and a 32.33% WER for the evaluation set for the AMI Corpus, both of which are the best results ever reported to the best of our knowledge.
@inproceedings{kanda2019acoustic, title = {Acoustic Modeling for Distant Multi-Talker Speech Recognition with Single- and Multi-Channel Branches}, author = {Kanda, Naoyuki and Fujita, Yusuke and Horiguchi, Shota and Ikeshita, Rintaro and Nagamatsu, Kenji and Watanabe, Shinji}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2019}, month = may, pages = {6630--6634}, }
WACV
Omnidirectional Pedestrian Detection by Rotation Invariant Training

Masato Tamura, Shota Horiguchi, and Tomokazu Murakami

In IEEE Winter Conference on Applications of Computer Vision (WACV), Jan 2019

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Recently much progress has been made in pedestrian detection by utilizing the learning ability of convolutional neural networks (CNNs). However, due to the lack of omnidirectional images to train CNNs, few CNN-based detectors have been proposed for omnidirectional pedestrian detection. One significant difference between omnidirectional images and perspective images is that the appearance of pedestrians is rotated in omnidirectional images. A previous method has dealt with this by transforming omnidirectional images into perspective images in the test phase. However, this method has significant drawbacks, namely, the computational cost and the performance degradation caused by the transformation. To address this issue, we propose a rotation invariant training method, which only uses randomly rotated perspective images without any additional annotation. By this method, existing large-scale datasets can be utilized. In test phase, omnidirectional images can be used without the transformation. To group predicted bounding boxes, we also develop a bounding box refinement, which works better for our detector than non-maximum suppression. The proposed detector achieved a state-of-the-art performance on four public benchmarks.
@inproceedings{tamura2019omnidirectional, title = {Omnidirectional Pedestrian Detection by Rotation Invariant Training}, booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)}, author = {Tamura, Masato and Horiguchi, Shota and Murakami, Tomokazu}, pages = {1989--1998}, year = {2019}, month = jan, }

2018

TMM
Personalized Classifier for Food Image Recognition

Shota Horiguchi, Sosuke Amano, Makoto Ogawa, and Kiyoharu Aizawa

IEEE Transactions on Multimedia, Oct 2018

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Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often has content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user’s domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework, which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.
@article{horiguchi2018personalized, title = {Personalized Classifier for Food Image Recognition}, journal = {IEEE Transactions on Multimedia}, author = {Horiguchi, Shota and Amano, Sosuke and Ogawa, Makoto and Aizawa, Kiyoharu}, volume = {20}, number = {10}, year = {2018}, month = oct, pages = {2836--2848}, }

ACMMM
Face-Voice Matching Using Cross-Modal Embeddings

Shota Horiguchi, Naoyuki Kanda, and Kenji Nagamatsu

In ACM International Conference on Multimedia (ACMMM), Oct 2018

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Face-voice matching is a task to find correspondence between faces and voices. Many researches in cognitive science have confirmed human ability in the face-voice matching tasks. Such ability is useful for creating natural human machine interaction systems and in many other applications. In this paper, we propose a face-voice matching model that learns cross-modal embeddings between face images and voice characteristics. We constructed a novel FVCeleb dataset which consists of face images and utterances from 1,078 persons. These persons were selected from the MS-Celeb-1M face image dataset and the VoxCeleb audio dataset. In two-alternative forced-choice matching task with an audio input and two face-image candidates of the same gender, our model achieved 62.2% and 56.5% accuracy on the FVCeleb and the subset of the GRID corpus, respectively. These results are very similar to human performance reported in cognitive science studies.
@inproceedings{horiguchi2018facevoice, title = {Face-Voice Matching Using Cross-Modal Embeddings}, booktitle = {ACM International Conference on Multimedia (ACMMM)}, author = {Horiguchi, Shota and Kanda, Naoyuki and Nagamatsu, Kenji}, pages = {1011-1019}, year = {2018}, month = oct, }

CHiME
The Hitachi/JHU CHiME-5 System: Advances in Speech Recognition for Everyday Home Environments Using Multiple Microphone Arrays

Naoyuki Kanda, Rintaro Ikeshita, Shota Horiguchi, Yusuke Fujita, Kenji Nagamatsu, Xiaofei Wang, Vimal Manohar, Nelson Enrique Yalta Soplin, Matthew Maciejewski, Szu-Jui Chen, Aswin Shanmugam Subramanian, Ruizhi Li, Zhiqi Wang, Jason Naradowsky, L. Paola Garcia-Perera, and Gregory Sell

In The 5th International Workshop on Speech Processing in Everyday Environments (CHiME-2018), Sep 2018

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This paper presents Hitachi and JHU’s efforts on developing CHiME-5 system to recognize dinner party speeches recorded by multiple microphone arrays. We newly developed (1) the way to apply multiple data augmentation methods, (2) residual bidirectional long short-term memory, (3) 4-ch acoustic models, (4) multiple-array combination methods, (5) hypothesis deduplication method, and (6) speaker adaptation technique of neural beamformer. As the results, our best system in category B achieved 52.38% of word error rates (WERs) for development set, which corresponded to 35% of relative WER reduction from the state-of-the-art baseline. Our best system also achieved 48.20% of WER for evaluation set, which was the 2nd best result in the CHiME-5 competition.
@inproceedings{kanda2018hitachijhu, title = {The {Hitachi/JHU} {CHiME-5} System: Advances in Speech Recognition for Everyday Home Environments Using Multiple Microphone Arrays}, author = {Kanda, Naoyuki and Ikeshita, Rintaro and Horiguchi, Shota and Fujita, Yusuke and Nagamatsu, Kenji and Wang, Xiaofei and Manohar, Vimal and {Yalta Soplin}, Nelson Enrique and Maciejewski, Matthew and Chen, Szu-Jui and Subramanian, Aswin Shanmugam and Li, Ruizhi and Wang, Zhiqi and Naradowsky, Jason and Garcia-Perera, L. Paola and Sell, Gregory}, booktitle = {The 5th International Workshop on Speech Processing in Everyday Environments (CHiME-2018)}, year = {2018}, month = sep, pages = {6--10}, }

2016

Food Search Based on User Feedback to Assist Image-Based Food Recording Systems

Sosuke Amano, Shota Horiguchi, Kiyoharu Aizawa, Kazuki Maeda, Masanori Kubota, and Makoto Ogawa

In International Workshop On Multimedia Assisted Dietary Management (MADiMa), Oct 2016

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Food diaries or diet journals are thought to be effective for improving the dietary lives of users. One important challenge in this field involves assisting users in recording their daily food intake. In recent years, food image recognition has attracted a considerable amount of research interest as a new technology to help record users ’food intake. However, since there are so many types of food, and it is unrealistic to expect a system to recognize all foods. In this paper, we propose an optimal combination of image recognition and interactive search in order to record users ’intake of food. The image recognition generates a list of candidate names for a given food picture. The user chooses the closest name to the meal, which triggers an associative food search based on food contents, such as ingredients. We show the proposed system is efficient to assist users maintain food journals.
@inproceedings{amano2016food, title = {Food Search Based on User Feedback to Assist Image-Based Food Recording Systems}, author = {Amano, Sosuke and Horiguchi, Shota and Aizawa, Kiyoharu and Maeda, Kazuki and Kubota, Masanori and Ogawa, Makoto}, booktitle = {International Workshop On Multimedia Assisted Dietary Management (MADiMa)}, pages = {71--75}, year = {2016}, month = oct, }
ICIP
The Log-Normal Distribution of the Size of Objects in Daily Meal Images and Its Application to the Efficient Reduction of Object Proposals

Shota Horiguchi, Kiyoharu Aizawa, and Makoto Ogawa

In IEEE International Conference on Image Processing (ICIP), Sep 2016

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In general, object-detection methods apply classifiers to pre-calculated object proposals. It is therefore important to minimize the number of proposals to achieve computational efficiency. In this paper, we show that the region size for food objects in recorded images of daily food follows a lognormal distribution, which is different from the distribution for widely used datasets collected by querying the names of dishes. We explain this characteristic using Gibrat’s law, and construct a model for the region-size distribution of objects in images. We applied the model to the filtering of object proposals generated by selective search and edge boxes. We obtained a significant reduction of 40.6% in the number of hypotheses compared with a conventional selective search, despite a decrease of only 0.007 in the Mean Average Best Overlap.
@inproceedings{horiguchi2016lognormal, title = {The Log-Normal Distribution of the Size of Objects in Daily Meal Images and Its Application to the Efficient Reduction of Object Proposals}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, author = {Horiguchi, Shota and Aizawa, Kiyoharu and Ogawa, Makoto}, pages = {3668--3672}, year = {2016}, month = sep, }