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Merging of Native and Non-native Speech for Low-resource Accented ASR

Abstract : This paper presents our recent study on low-resource automatic speech recognition (ASR) system with accented speech. We propose multi-accent Subspace Gaussian Mixture Models (SGMM) and accent-specific Deep Neural Networks (DNN) for improving non-native ASR performance. In the SGMM framework, we present an original language weighting strategy to merge the globally shared parameters of two models based on native and non-native speech respectively. In the DNN framework, a native deep neural net is fine-tuned to non-native speech. Over the non-native baseline, we achieved relative improvement of 15 % for multi-accent SGMM and 34 % for accent-specific DNN with speaker adaptation.
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Submitted on : Monday, January 29, 2018 - 10:57:21 AM
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Sarah Samson Juan, Laurent Besacier, Benjamin Lecouteux, Tien-Ping Tan. Merging of Native and Non-native Speech for Low-resource Accented ASR. 3rd International Conference on Statistical Language and Speech Processing, SLSP 2015, Nov 2015, Budapest, Hungary. ⟨10.1007/978-3-319-25789-1_24⟩. ⟨hal-01289140⟩



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