publications (ja)
2024
- ASJImproving the Naturalness of Simulated Conversations for End-to-End Neural DiarizationNatsuo Yamashita, Shota Horiguchi, and Takeshi HommaIn The 2024 Autumn Meeting of the Acoustical Society of Japan, Sep 2024
- ASJSpeaker Embedding Extraction from Multi-Speaker RecordingsShota Horiguchi, Atsushi Ando, Takafumi Moriya, Takanori Ashihara, Hiroshi Sato, Naohiro Tawara, and Marc DelcroixIn The 2024 Autumn Meeting of the Acoustical Society of Japan, Sep 2024
2023
- IBISStreaming Active Learning for Regression Problems Using Regression via ClassificationShota Horiguchi, Kota Dohi, and Yohei KawaguchiIn The 26th Information-Based Induction Sciences Workshop, Oct 2023
- ASJRisk Assessment of Spoofing Attacks Using Self-Supervised Learning Models on Speaker Verification SystemsAoi Ito and Shota HoriguchiIn The 2023 Autumn Meeting of the Acoustical Society of Japan, Sep 2023🏆 ASJ Best Student Presentation Award
- ASJBlock-Online Speaker Diarization for Unlimited Numbers of SpeakersShota Horiguchi, Shinji Watanabe, Paola Garcia, Yuki Takashima, and Yohei KawaguchiIn The 2023 Autumn Meeting of the Acoustical Society of Japan, Sep 2023
- ASJMutual Learning of Single- and Multi-Channel Speaker Diarization ModelsShota Horiguchi, Yuki Takashima, Shinji Watanabe, and Paola GarciaIn The 2023 Spring Meeting of the Acoustical Society of Japan, Mar 2023
2022
- ASJPreventing Catastrophic Forgetting by Partial Fine-Tuning for Continual Learning of End-to-End ASRYuki Takashima, Shota Horiguchi, Shinji Watanabe, Paola Garcia, and Yohei KawaguchiIn The 2022 Autumn Meeting of the Acoustical Society of Japan, Sep 2022
- ASJMulti-Channel Neural Diarization Using Distributed MicrophonesShota Horiguchi, Yuki Takashima, Paola Garcia, Shinji Watanabe, and Yohei KawaguchiIn The 2022 Autumn Meeting of the Acoustical Society of Japan, Sep 2022
- ASJEnvironmental Sound Extraction Using OnomatopoeiaYuki Okamoto, Shota Horiguchi, Masaaki Yamamoto, Keisuke Imoto, and Yohei KawaguchiIn The 2022 Spring Meeting of the Acoustical Society of Japan, Mar 2022
- ASJSemi-Supervised Adaptation with Pseudo-Labeling for End-to-End Neural DiarizationYuki Takashima, Yusuke Fujita, Shota Horiguchi, Shinji Watanabe, Paola Garcia, and Kenji NagamatsuIn The 2022 Spring Meeting of the Acoustical Society of Japan, Mar 2022
- ASJNeural Diarization for Unlimited Number of Speakers Using Global and Local AttractorsShota Horiguchi, Shinji Watanabe, Paola Garcia, Yawen Xue, Yuki Takashima, and Yohei KawaguchiIn The 2022 Spring Meeting of the Acoustical Society of Japan, Mar 2022
2017
2016
- MIRULarge Scale Meal Image Recognition via Personalized ClassifiersShota Horiguchi, Sosuke Amano, Kiyoharu Aizawa, and Makoto OgawaIn The 19th Meeting on Image Recognition and Understanding (MIRU), Aug 2016
2015
- ITEA Discussion of Novelty Detection in Image RecognitionMichihiro Mizuno, Akito Takeki, Shota Horiguchi, Toshihiko Yamasaki, and Kiyoharu AizawaIn The ITE Winter Annual Convention, Dec 2015🏆 ITE Outstanding Student Presentation Award
We present a new method of novelty detection in image recognition based on convolutional neural network (CNN). We use Sigmoid Layer as the last layer of a CNN instead of Softmax Layer. As a result, we discovered that a CNN with Sigmoid Layer can detect novelties in an easy dataset better than that with Softmax Layer, but worse in a difficult dataset.
- DEA Study on Hierarchical Food ClassificationHokuto Kagaya, Shota Horiguchi, Sosuke Amanom, and Kiyoharu AizawaIn IEICE Technical Comittee on Data Engineering (DE), Sep 2015
Automatic food recognition or classification is very challenging task. One of the reason is that the number of food items is enormous, and so we can’t easily choose a single label for each food image. To solve this problem, we have studied hierarchical food classification. In this paper, we investigate the benefits of introducing hierarchy to classication and try to build hierarchy from words of food names automatically. As a result, we observed the difference between data used for training.
- MIRUSelective Removal of Object Window Hypethesis Using GrabCutShota Horiguchi, Kiyoharu Aizawa, and Makoto OgawaIn The 18th Meeting on Image Recognition and Understanding (MIRU), Jul 2015
- PRMULog-Normal Distribution of Objects’ Size in Images and Its Applications to Object Detection—Comparing General Images and Food ImagesShota Horiguchi, Kiyoharu Aizawa, and Makoto OgawaIn IEICE Technical Committee on Pattern Recognition and Media Understanding (PRMU), Mar 2015
When detecting objects in images by which classifying many location hypotheses, it is necessary to define aspect ratio and scale of detection window in advance. In this paper, we construct a model of size distribution of objects in images, and revealed that the size of foods in images taken for recording dietary follows a log-normal distribution. We apply this characteristic to define parameters of selective search, the method of generating object hypotheses, and result in high Mean Average Best Overlap in spite of small number of object hypotheses when using FoodLog image dataset.