I am a Research Specialist at NTT Human Informatics Laboratories. My research interests are not limited to speech-related technology such as speech recognition, speech separation, and speaker diarization, but also include image processing and multimodal processing.

Prior to joining NTT, I was a Senior Researcher at Hitachi, Ltd. Research and Development Group from 2017 to 2024.

I received my Ph.D. degree in 2023 from University of Tsukuba under the supervision of Prof. Takeshi Yamada. At University of Tsukuba, I was a member of the Multimedia Laboratory.

Before that, I studied computer vision at the Aizawa-Yamasaki Laboratory (now Aizawa-Yamakata-Matsui Laboratory) of the University of Tokyo, where I received B.E. and M.E. degrees under the supervision of Prof. Kiyoharu Aizawa.

news

Aug 31, 2024	One first-authored paper and one co-authored paper have been accepted to SLT 2024.
Jun 8, 2024	Two co-authored papers have been accepted to INTERSPEECH 2024.
Feb 1, 2024	I’m starting a new position as Research Specialist at NTT Human Informatics Laboratories.
Dec 14, 2023	Our paper “Streaming Active Learning for Regression Problems Using Regression via Classification” has been accepted to ICASSP 2024.
Sep 14, 2023	My PhD thesis is now open.

selected publications

1. 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

   Abs arXiv Bib HTML

   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},
   }

2. 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

   Abs arXiv Bib HTML

   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},
   }

3. 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

   Abs arXiv Bib HTML

   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},
   }

4. TMM

   Personalized Classifier for Food Image Recognition

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

   IEEE Transactions on Multimedia, Oct 2018

   Abs arXiv Bib HTML

   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},
   }