NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909309073358848 |
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| author | Kamo, Naoyuki Tawara, Naohiro Ando, Atsushi Kano, Takatomo Sato, Hiroshi Ikeshita, Rintaro Moriya, Takafumi Horiguchi, Shota Matsuura, Kohei Ogawa, Atsunori Plaquet, Alexis Ashihara, Takanori Ochiai, Tsubasa Mimura, Masato Delcroix, Marc Nakatani, Tomohiro Asami, Taichi Araki, Shoko |
| author_facet | Kamo, Naoyuki Tawara, Naohiro Ando, Atsushi Kano, Takatomo Sato, Hiroshi Ikeshita, Rintaro Moriya, Takafumi Horiguchi, Shota Matsuura, Kohei Ogawa, Atsunori Plaquet, Alexis Ashihara, Takanori Ochiai, Tsubasa Mimura, Masato Delcroix, Marc Nakatani, Tomohiro Asami, Taichi Araki, Shoko |
| contents | We present a distant automatic speech recognition (DASR) system developed for the CHiME-8 DASR track. It consists of a diarization first pipeline. For diarization, we use end-to-end diarization with vector clustering (EEND-VC) followed by target speaker voice activity detection (TS-VAD) refinement. To deal with various numbers of speakers, we developed a new multi-channel speaker counting approach. We then apply guided source separation (GSS) with several improvements to the baseline system. Finally, we perform ASR using a combination of systems built from strong pre-trained models. Our proposed system achieves a macro tcpWER of 21.3 % on the dev set, which is a 57 % relative improvement over the baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge Kamo, Naoyuki Tawara, Naohiro Ando, Atsushi Kano, Takatomo Sato, Hiroshi Ikeshita, Rintaro Moriya, Takafumi Horiguchi, Shota Matsuura, Kohei Ogawa, Atsunori Plaquet, Alexis Ashihara, Takanori Ochiai, Tsubasa Mimura, Masato Delcroix, Marc Nakatani, Tomohiro Asami, Taichi Araki, Shoko Audio and Speech Processing We present a distant automatic speech recognition (DASR) system developed for the CHiME-8 DASR track. It consists of a diarization first pipeline. For diarization, we use end-to-end diarization with vector clustering (EEND-VC) followed by target speaker voice activity detection (TS-VAD) refinement. To deal with various numbers of speakers, we developed a new multi-channel speaker counting approach. We then apply guided source separation (GSS) with several improvements to the baseline system. Finally, we perform ASR using a combination of systems built from strong pre-trained models. Our proposed system achieves a macro tcpWER of 21.3 % on the dev set, which is a 57 % relative improvement over the baseline. |
| title | NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.05554 |