Intelligibility prediction with a pretrained noise-robust automatic speech recognition model

2023-10-20 · Zehai Tu, Ning Ma, Jon Barker

This paper describes two intelligibility prediction systems derived from a pretrained noise-robust automatic speech recognition (ASR) model for the second Clarity Prediction Challenge (CPC2). One system is intrusive and leverages the hidden representations of the ASR model. The other system is non-intrusive and makes predictions with derived ASR uncertainty. The ASR model is only pretrained with a simulated noisy speech corpus and does not take advantage of the CPC2 data. For that reason, the intelligibility prediction systems are robust to unseen scenarios given the accurate prediction performance on the CPC2 evaluation.

📄 PDF Abstract BibTeX arXiv:2310.19817

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Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Prediction speech-recognition Speech Recognition

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