M
Marc Delcroix
Researcher at Nippon Telegraph and Telephone
Publications - 189
Citations - 5075
Marc Delcroix is an academic researcher from Nippon Telegraph and Telephone. The author has contributed to research in topics: Speech enhancement & Artificial neural network. The author has an hindex of 31, co-authored 189 publications receiving 3679 citations. Previous affiliations of Marc Delcroix include NTT Communications Corp & Hokkaido University.
Papers
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Journal ArticleDOI
A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research
Keisuke Kinoshita,Marc Delcroix,Sharon Gannot,Emanuel A. P. Habets,Reinhold Haeb-Umbach,Walter Kellermann,Volker Leutnant,Roland Maas,Tomohiro Nakatani,Bhiksha Raj,Armin Sehr,Takuya Yoshioka +11 more
TL;DR: The REVERB challenge is described, which is an evaluation campaign that was designed to evaluate such speech enhancement and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions.
Proceedings ArticleDOI
The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices
Takuya Yoshioka,Nobutaka Ito,Marc Delcroix,Atsunori Ogawa,Keisuke Kinoshita,Masakiyo Fujimoto,Chengzhu Yu,Wojciech J. Fabian,Miquel Espi,Takuya Higuchi,Shoko Araki,Tomohiro Nakatani +11 more
TL;DR: NTT's CHiME-3 system is described, which integrates advanced speech enhancement and recognition techniques, which achieves a 3.45% development error rate and a 5.83% evaluation error rate.
Journal ArticleDOI
Making Machines Understand Us in Reverberant Rooms: Robustness Against Reverberation for Automatic Speech Recognition
Takuya Yoshioka,Armin Sehr,Marc Delcroix,Keisuke Kinoshita,Roland Maas,Tomohiro Nakatani,Walter Kellermann +6 more
TL;DR: For a number of unexplored but important applications, distant microphones are a prerequisite for extending the availability of speech recognizers as well as enhancing the convenience of existing speech recognition applications.
Proceedings ArticleDOI
Improving transformer-based end-to-end speech recognition with connectionist temporal classification and language model integration
TL;DR: This work integrates connectionist temporal classification (CTC) with Transformer for joint training and decoding of automatic speech recognition (ASR) tasks and makes training faster than with RNNs and assists LM integration.
Journal ArticleDOI
Suppression of Late Reverberation Effect on Speech Signal Using Long-Term Multiple-step Linear Prediction
TL;DR: A room impulse response is assumed to consist of three parts: a direct-path response, early reflections and late reverberations, which is known to be a major cause of ASR performance degradation.