文章基本信息

标题：Accent neutralization for non-native speech using neural style transfer
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作者：Kacper Radzikowski
期刊名称：Telecommunications System & Management
电子版ISSN：2167-0919
出版年度：2020
卷号：9
期号：3
页码：16-16
DOI：10.37421jtsm.2020.9.296
语种：English
出版社：OMICS Group
摘要：Automatic speech recognition (ASR) has been an object of extensive research since the second half of the previous century. ASR systems achieve high accuracy rates, however, only when the system is used for recognizing the speech of native speakers. The score drops in case the ASR system is being used with a non-native speaker of the language to be recognized, as the pronunciation is affected by the patterns of the mother tongue. Traditional approaches for developing speech recognition classifiers are based on supervised learning, relying on the existence of large labeled datasets. In case of non-native speech such datasets do not always exist and even if they do, the number of samples is not always high enough to train accurate classifiers. We have dealt with the problem of the non-native speech in our previous research using different approach of dual-supervised learning [1