2022 IEEE Spoken Language Technology Workshop (SLT) · 2023 · 21 citations · 14 references
Health ProfessionsSpeech-language PathologyComputer VisionHearing AidsPsychoacousticsSpeech IntelligibilitySpeech Intelligibility EnhancementSpeech RecognitionAudio-visual Speech RecognitionBenchmark DatasetsSpeech EnhancementSpeech Enhancement AlgorithmsSpeech Intelligibility MeasurementSpeech Signal AnalysisSpeech Signal EnhancementSpeech Signal Processing
Audio-visual speech enhancement is the task of improving the quality of a speech signal when video of the speaker is available. It opens-up the opportunity of improving speech intelligibility in adverse listening scenarios that are currently too challenging for audio-only speech enhancement models. The Audio-Visual Speech Enhancement (AVSE) challenge aims to set the first benchmark in this area. We provide participants with datasets and scripts to test their audio-visual speech enhancement models under a common framework for both training and evaluation. The data is derived from real-world videos, and comprises noisy mixes, in which audio from target speaker is mixed with either a competing speaker or a noise signal. The submitted systems are evaluated by conducting AV intelligibility tests involving human participants. We expect this challenge to be a platform for advancing the field of audio-visual speech-enhancement and to provide further insight about the scope and limitations of current AV speech enhancement approaches.
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · 2016 · 222.1K citations · Full text
A short-time objective intelligibility measure for time-frequency weighted noisy speech
Cees Taal, Richard C. Hendriks, Richard Heusdens et al. · 2010 · 1.1K citations
DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement