no code implementations • 23 Jun 2023 • Samuele Cornell, Matthew Wiesner, Shinji Watanabe, Desh Raj, Xuankai Chang, Paola Garcia, Matthew Maciejewski, Yoshiki Masuyama, Zhong-Qiu Wang, Stefano Squartini, Sanjeev Khudanpur
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 6 Nov 2022 • Jiatong Shi, Chan-Jan Hsu, Holam Chung, Dongji Gao, Paola Garcia, Shinji Watanabe, Ann Lee, Hung-Yi Lee
To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 13 Oct 2022 • Yen Meng, Hsuan-Jui Chen, Jiatong Shi, Shinji Watanabe, Paola Garcia, Hung-Yi Lee, Hao Tang
Subsampling while training self-supervised models not only improves the overall performance on downstream tasks under certain frame rates, but also brings significant speed-up in inference.
no code implementations • 7 Oct 2022 • Shota Horiguchi, Yuki Takashima, Shinji Watanabe, Paola Garcia
This paper focuses on speaker diarization and proposes to conduct the above bi-directional knowledge transfer alternately.
no code implementations • 6 Jun 2022 • Shota Horiguchi, Shinji Watanabe, Paola Garcia, Yuki Takashima, Yohei Kawaguchi
Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND.
no code implementations • 15 Mar 2022 • Zili Huang, Shinji Watanabe, Shu-wen Yang, Paola Garcia, Sanjeev Khudanpur
Speech enhancement and separation are two fundamental tasks for robust speech processing.
no code implementations • 10 Oct 2021 • Shota Horiguchi, Yuki Takashima, Paola Garcia, Shinji Watanabe, Yohei Kawaguchi
With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel input was given while maintaining comparable performance with a single-channel input.
no code implementations • 4 Jul 2021 • Shota Horiguchi, Shinji Watanabe, Paola Garcia, Yawen Xue, Yuki Takashima, Yohei Kawaguchi
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.
no code implementations • 20 Jun 2021 • Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, Paola Garcia
Diarization results are then estimated as dot products of the attractors and embeddings.
no code implementations • 2 Feb 2021 • Shota Horiguchi, Nelson Yalta, Paola Garcia, Yuki Takashima, Yawen Xue, Desh Raj, Zili Huang, Yusuke Fujita, Shinji Watanabe, Sanjeev Khudanpur
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge.
no code implementations • 21 Jan 2021 • Yawen Xue, Shota Horiguchi, Yusuke Fujita, Yuki Takashima, Shinji Watanabe, Paola Garcia, Kenji Nagamatsu
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.
Speaker Diarization Sound Audio and Speech Processing
no code implementations • 18 Dec 2020 • Shota Horiguchi, Paola Garcia, Yusuke Fujita, Shinji Watanabe, Kenji Nagamatsu
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.
1 code implementation • 14 Feb 2020 • Zili Huang, Shinji Watanabe, Yusuke Fujita, Paola Garcia, Yiwen Shao, Daniel Povey, Sanjeev Khudanpur
Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem.
1 code implementation • 2 Dec 2019 • Paola Garcia, Jesus Villalba, Herve Bredin, Jun Du, Diego Castan, Alejandrina Cristia, Latane Bullock, Ling Guo, Koji Okabe, Phani Sankar Nidadavolu, Saurabh Kataria, Sizhu Chen, Leo Galmant, Marvin Lavechin, Lei Sun, Marie-Philippe Gill, Bar Ben-Yair, Sajjad Abdoli, Xin Wang, Wassim Bouaziz, Hadrien Titeux, Emmanuel Dupoux, Kong Aik Lee, Najim Dehak
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios.
Audio and Speech Processing Sound