CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Multilingual Tandem Bottleneck Feature For Language Identification
Wang Geng; Jie Li,; Shanshan Zhang; Xinyuan Cai; Bo Xu; Xinyuan.Cai
2016-09
Conference NameInterspeech 2015
Source PublicationInterspeech2015
Conference Date2016.9.6-2016.9.10
Conference PlaceDresden,German
AbstractThe deep bottleneck (BN) feature based ivector solution has
been recognized as a popular pipeline for language identification
(LID) recently. However, issues such as how to extract
more effective BN features and how to fully utilize features
extracted from deep neural networks (DNN) are still not well
investigated. In this paper, these issues are empirically tackled
by means as follows: First, two novel types of deep features,
phone-discriminant and triphone-discriminate are extracted.
Then, DNNs are trained both separately and jointly on multilingual
corpuses to produce different BN features. Finally, tandem
fashion on deep BN features is applied to build enhanced
deep features. Experiment results show that systems built on
top of tandem deep features obtain 19% and 42% relative equal
error rate reduction on average on NIST LRE 2007 over the
counterpart built on traditional deep BN features and the cepstral
feature based LID system, respectively
KeywordLanguage Identification Deep Bottleneck Feature Tandem Feature Multi-deep Feature Multi-training Procedure.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12485
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorXinyuan.Cai
AffiliationInstitute of Automation Chinese Academy of Science
Recommended Citation
GB/T 7714
Wang Geng,Jie Li,,Shanshan Zhang,et al. Multilingual Tandem Bottleneck Feature For Language Identification[C],2016.
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