Merging of native and non-native speech for low-resource accented ASR
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 pres...
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| Main Authors: | , , , |
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| Format: | E-Article |
| Language: | English |
| Published: |
Springer Verlag
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/12098/1/No%2035%20%28abstrak%29.pdf http://ir.unimas.my/id/eprint/12098/ http://www.scopus.com/inward/record.url?eid=2-s2.0-84952362047&partnerID=40&md5=6bc512988afc29cd7ca4af16a836f0b3 |
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| Summary: | 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 espectively. 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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