Ms. Le Wang Profile

Le Wang

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Proceedings Article | 6 November 2019 Paper

Non-native speech recognition using audio style transfer

Kacper Radzikowski, Mateusz Forc, Le Wang, Osamu Yoshie, Robert Nowak

Proceedings Volume 11176, 111762J (2019) https://doi.org/10.1117/12.2536535

KEYWORDS: Speech recognition, Neural networks, Machine learning, Computer science, Computing systems, Tongue, Convolutional neural networks, Databases, Fourier transforms, Statistical modeling

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Recently automatic speech recognition (ASR) systems achieve higher and higher accuracy rates. However, the score drops significantly, when the ASR system is being used with a non-native speaker of the language to be recognized, mainly because of specific pronunciation and accent features. A limited volume of labeled datasets containing samples of a non-native speech makes it difficult to train any new ASR systems targeted for non-native speakers. In our research, we tried tackling the problem of a non-native accent and its influence on the accuracy of ASR systems, using the style transfer methodology. We designed a pipeline for modifying the speech produced by a nonnative speaker, so that it resembles the native speech to a higher extent, i.e. a method for accent neutralization. Our methodology can be used as a wrapper for any existing ASR system, which reduces the necessity of training new speech recognizers, adapted for non-native speech. The modification can be thus performed on the fly, before passing the data forward to the speech recognition system itself.

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