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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer Verlag
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/12098/ http://ir.unimas.my/id/eprint/12098/1/No%2035%20%28abstrak%29.pdf |
| _version_ | 1848837126976700416 |
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| author | Samson Juan, Sarah Besacier, Laurent Lecouteux, Benjamin Tien-Ping, Tan |
| author_facet | Samson Juan, Sarah Besacier, Laurent Lecouteux, Benjamin Tien-Ping, Tan |
| author_sort | Samson Juan, Sarah |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T06:34:43Z |
| format | Article |
| id | unimas-12098 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:34:43Z |
| publishDate | 2015 |
| publisher | Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-120982016-10-21T07:34:47Z http://ir.unimas.my/id/eprint/12098/ Merging of native and non-native speech for low-resource accented ASR Samson Juan, Sarah Besacier, Laurent Lecouteux, Benjamin Tien-Ping, Tan T Technology (General) 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. Springer Verlag 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/12098/1/No%2035%20%28abstrak%29.pdf Samson Juan, Sarah and Besacier, Laurent and Lecouteux, Benjamin and Tien-Ping, Tan (2015) Merging of native and non-native speech for low-resource accented ASR. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9449. pp. 255-266. ISSN 3029743 http://www.scopus.com/inward/record.url?eid=2-s2.0-84952362047&partnerID=40&md5=6bc512988afc29cd7ca4af16a836f0b3 DOI: 10.1007/978-3-319-25789-1 24 |
| spellingShingle | T Technology (General) Samson Juan, Sarah Besacier, Laurent Lecouteux, Benjamin Tien-Ping, Tan Merging of native and non-native speech for low-resource accented ASR |
| title | Merging of native and non-native speech for low-resource accented ASR |
| title_full | Merging of native and non-native speech for low-resource accented ASR |
| title_fullStr | Merging of native and non-native speech for low-resource accented ASR |
| title_full_unstemmed | Merging of native and non-native speech for low-resource accented ASR |
| title_short | Merging of native and non-native speech for low-resource accented ASR |
| title_sort | merging of native and non-native speech for low-resource accented asr |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/12098/ http://ir.unimas.my/id/eprint/12098/ http://ir.unimas.my/id/eprint/12098/ http://ir.unimas.my/id/eprint/12098/1/No%2035%20%28abstrak%29.pdf |