Speech recognition in adverse conditions by humans and machines

Abstract

In the development of automatic speech recognition systems, achieving human-like performance has been a long-held goal. Recent releases of large spoken language models have claimed to achieve such performance, although direct comparison to humans has been severely limited. The present study tested L1 British English listeners against two automatic speech recognition systems (wav2vec 2.0 and Whisper, base and large sizes) in adverse listening conditions: speech-shaped noise and pub noise, at different signal-to-noise ratios, and recordings produced with or without face masks. Humans maintained the advantage against all systems, except for Whisper large, which outperformed humans in every condition but pub noise.