Article ; Online: Automatic recognition of second language speech-in-noise.
JASA express letters
2024 Volume 4, Issue 2
Abstract: Measuring how well human listeners recognize speech under varying environmental conditions (speech intelligibility) is a challenge for theoretical, technological, and clinical approaches to speech communication. The current gold standard-human ... ...
Abstract | Measuring how well human listeners recognize speech under varying environmental conditions (speech intelligibility) is a challenge for theoretical, technological, and clinical approaches to speech communication. The current gold standard-human transcription-is time- and resource-intensive. Recent advances in automatic speech recognition (ASR) systems raise the possibility of automating intelligibility measurement. This study tested 4 state-of-the-art ASR systems with second language speech-in-noise and found that one, whisper, performed at or above human listener accuracy. However, the content of whisper's responses diverged substantially from human responses, especially at lower signal-to-noise ratios, suggesting both opportunities and limitations for ASR--based speech intelligibility modeling. |
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MeSH term(s) | Humans ; Speech Perception/physiology ; Noise/adverse effects ; Speech Intelligibility/physiology ; Speech Recognition Software ; Recognition, Psychology |
Language | English |
Publishing date | 2024-02-13 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 2691-1191 |
ISSN (online) | 2691-1191 |
DOI | 10.1121/10.0024877 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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