Deep bottleneck features for spoken language identification.
A key problem in spoken language identification (LID) is to design effective representations which are specific to language information. For example, in recent years, representations based on both phonotactic and acoustic features have proven their effectiveness for LID. Although advances in machine...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24983963/pdf/?tool=EBI |
| _version_ | 1819181017578078208 |
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| author | Bing Jiang Yan Song Si Wei Jun-Hua Liu Ian Vince McLoughlin Li-Rong Dai |
| author_facet | Bing Jiang Yan Song Si Wei Jun-Hua Liu Ian Vince McLoughlin Li-Rong Dai |
| author_sort | Bing Jiang |
| collection | DOAJ |
| description | A key problem in spoken language identification (LID) is to design effective representations which are specific to language information. For example, in recent years, representations based on both phonotactic and acoustic features have proven their effectiveness for LID. Although advances in machine learning have led to significant improvements, LID performance is still lacking, especially for short duration speech utterances. With the hypothesis that language information is weak and represented only latently in speech, and is largely dependent on the statistical properties of the speech content, existing representations may be insufficient. Furthermore they may be susceptible to the variations caused by different speakers, specific content of the speech segments, and background noise. To address this, we propose using Deep Bottleneck Features (DBF) for spoken LID, motivated by the success of Deep Neural Networks (DNN) in speech recognition. We show that DBFs can form a low-dimensional compact representation of the original inputs with a powerful descriptive and discriminative capability. To evaluate the effectiveness of this, we design two acoustic models, termed DBF-TV and parallel DBF-TV (PDBF-TV), using a DBF based i-vector representation for each speech utterance. Results on NIST language recognition evaluation 2009 (LRE09) show significant improvements over state-of-the-art systems. By fusing the output of phonotactic and acoustic approaches, we achieve an EER of 1.08%, 1.89% and 7.01% for 30 s, 10 s and 3 s test utterances respectively. Furthermore, various DBF configurations have been extensively evaluated, and an optimal system proposed. |
| first_indexed | 2024-12-22T22:23:33Z |
| format | Article |
| id | doaj.art-0d393633184b41409e7ad15df36a83c7 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T22:23:33Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0d393633184b41409e7ad15df36a83c72022-12-21T18:10:37ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0197e10079510.1371/journal.pone.0100795Deep bottleneck features for spoken language identification.Bing JiangYan SongSi WeiJun-Hua LiuIan Vince McLoughlinLi-Rong DaiA key problem in spoken language identification (LID) is to design effective representations which are specific to language information. For example, in recent years, representations based on both phonotactic and acoustic features have proven their effectiveness for LID. Although advances in machine learning have led to significant improvements, LID performance is still lacking, especially for short duration speech utterances. With the hypothesis that language information is weak and represented only latently in speech, and is largely dependent on the statistical properties of the speech content, existing representations may be insufficient. Furthermore they may be susceptible to the variations caused by different speakers, specific content of the speech segments, and background noise. To address this, we propose using Deep Bottleneck Features (DBF) for spoken LID, motivated by the success of Deep Neural Networks (DNN) in speech recognition. We show that DBFs can form a low-dimensional compact representation of the original inputs with a powerful descriptive and discriminative capability. To evaluate the effectiveness of this, we design two acoustic models, termed DBF-TV and parallel DBF-TV (PDBF-TV), using a DBF based i-vector representation for each speech utterance. Results on NIST language recognition evaluation 2009 (LRE09) show significant improvements over state-of-the-art systems. By fusing the output of phonotactic and acoustic approaches, we achieve an EER of 1.08%, 1.89% and 7.01% for 30 s, 10 s and 3 s test utterances respectively. Furthermore, various DBF configurations have been extensively evaluated, and an optimal system proposed.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24983963/pdf/?tool=EBI |
| spellingShingle | Bing Jiang Yan Song Si Wei Jun-Hua Liu Ian Vince McLoughlin Li-Rong Dai Deep bottleneck features for spoken language identification. PLoS ONE |
| title | Deep bottleneck features for spoken language identification. |
| title_full | Deep bottleneck features for spoken language identification. |
| title_fullStr | Deep bottleneck features for spoken language identification. |
| title_full_unstemmed | Deep bottleneck features for spoken language identification. |
| title_short | Deep bottleneck features for spoken language identification. |
| title_sort | deep bottleneck features for spoken language identification |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24983963/pdf/?tool=EBI |
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