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Deep Bottleneck Network based i-vector representation for Language Identification

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. (Contact us about this Publication)
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)
Subjects: T Technology
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 21 Aug 2015 10:08 UTC
Last Modified: 29 May 2019 15:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50262 (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince: https://orcid.org/0000-0001-7111-2008
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