Song, Yan, Hong, Xinhai, Jiang, Bing, Cui, Ruilian, McLoughlin, Ian Vince, Dai, Lirong (2015) Deep Bottleneck Network based i-vector representation for Language Identification. In: Proc. Interspeech 2015. . (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:50262)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: http://www.interspeech2015.org |
Abstract
This paper presents a unified i-vector framework for language identification (LID) based on deep bottleneck networks (DBN) trained for automatic speech recognition (ASR). The framework covers both front-end feature extraction and back-end modeling stages.The output from different layers of a DBN are exploited to improve the effectiveness of the i-vector representation through incorporating a mixture of acoustic and phonetic information. Furthermore, a universal model is derived from the DBN with a LID corpus. This is a somewhat inverse process to the GMM-UBM method, in which the GMM of each language is mapped from a GMM-UBM. Evaluations on specific dialect recognition tasks show that the DBN based i-vector can achieve significant and consistent performance gains over conventional GMM-UBM and DNN based i-vector methods. The generalization capability of this framework is also evaluated using DBNs trained on Mandarin and English corpuses.
Index Terms: Language Identification, Deep Neural Network, Deep Bottleneck Feature, i-vector representation
Item Type: | Conference or workshop item (Speech) |
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Subjects: | T Technology |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Ian McLoughlin |
Date Deposited: | 21 Aug 2015 10:08 UTC |
Last Modified: | 17 Aug 2022 10:59 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/50262 (The current URI for this page, for reference purposes) |
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