Brouhaha: Multi-Task Training for Voice Activity Detection, Speech-to-Noise Ratio, and C50 Room Acoustics Estimation-Reference-Cited by-同舟云学术

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Brouhaha: Multi-Task Training for Voice Activity Detection, Speech-to-Noise Ratio, and C50 Room Acoustics Estimation

Published:2023-12-16 Issue: Volume: Page:1-7
ISSN:
Container-title:2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
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Author:

Lavechin Marvin¹,Métais Marianne¹,Titeux Hadrien¹,Boissonnet Alodie²,Copet Jade²,Rivière Morgane²,Bergelson Elika³,Cristia Alejandrina¹,Dupoux Emmanuel¹,Bredin Hervé⁴

Affiliation:

1. PSL University,LSCP, DEC, ENS, EHESS, CNRS,Paris,France

2. Meta AI Research,France

3. Duke University,North Carolina,USA

4. IRIT, Université de Toulouse,CNRS,Toulouse,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10388490/10389614/10389718.pdf?arnumber=10389718

Reference34 articles.

1. Improving speech recognition in reverberation using a room-aware deep neural network and multi-task learning

2. A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

3. Predicting Word Error Rate for Reverberant Speech

4. Robust Speaker Identification in Noisy and Reverberant Conditions

5. The Third DIHARD Diarization Challenge

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge;Computer Speech & Language;2025-01

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