NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription
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| Natura: | Preprint |
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2024
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| author | Vinnikov, Alon Ivry, Amir Hurvitz, Aviv Abramovski, Igor Koubi, Sharon Gurvich, Ilya Pe`er, Shai Xiao, Xiong Elizalde, Benjamin Martinez Kanda, Naoyuki Wang, Xiaofei Shaer, Shalev Yagev, Stav Asher, Yossi Sivasankaran, Sunit Gong, Yifan Tang, Min Wang, Huaming Krupka, Eyal |
| author_facet | Vinnikov, Alon Ivry, Amir Hurvitz, Aviv Abramovski, Igor Koubi, Sharon Gurvich, Ilya Pe`er, Shai Xiao, Xiong Elizalde, Benjamin Martinez Kanda, Naoyuki Wang, Xiaofei Shaer, Shalev Yagev, Stav Asher, Yossi Sivasankaran, Sunit Gong, Yifan Tang, Min Wang, Huaming Krupka, Eyal |
| contents | We introduce the first Natural Office Talkers in Settings of Far-field Audio Recordings (``NOTSOFAR-1'') Challenge alongside datasets and baseline system. The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics. It is recorded across 30 conference rooms, featuring 4-8 attendees and a total of 35 unique speakers. Second, a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions. The tasks focus on single-device DASR, where multi-channel devices always share the same known geometry. This is aligned with common setups in actual conference rooms, and avoids technical complexities associated with multi-device tasks. It also allows for the development of geometry-specific solutions. The NOTSOFAR-1 Challenge aims to advance research in the field of distant conversational speech recognition, providing key resources to unlock the potential of data-driven methods, which we believe are currently constrained by the absence of comprehensive high-quality training and benchmarking datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08887 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription Vinnikov, Alon Ivry, Amir Hurvitz, Aviv Abramovski, Igor Koubi, Sharon Gurvich, Ilya Pe`er, Shai Xiao, Xiong Elizalde, Benjamin Martinez Kanda, Naoyuki Wang, Xiaofei Shaer, Shalev Yagev, Stav Asher, Yossi Sivasankaran, Sunit Gong, Yifan Tang, Min Wang, Huaming Krupka, Eyal Sound Artificial Intelligence Computation and Language Audio and Speech Processing We introduce the first Natural Office Talkers in Settings of Far-field Audio Recordings (``NOTSOFAR-1'') Challenge alongside datasets and baseline system. The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics. It is recorded across 30 conference rooms, featuring 4-8 attendees and a total of 35 unique speakers. Second, a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions. The tasks focus on single-device DASR, where multi-channel devices always share the same known geometry. This is aligned with common setups in actual conference rooms, and avoids technical complexities associated with multi-device tasks. It also allows for the development of geometry-specific solutions. The NOTSOFAR-1 Challenge aims to advance research in the field of distant conversational speech recognition, providing key resources to unlock the potential of data-driven methods, which we believe are currently constrained by the absence of comprehensive high-quality training and benchmarking datasets. |
| title | NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription |
| topic | Sound Artificial Intelligence Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2401.08887 |