UNSUPERVISED TRAINING OF SUBSPACE GAUSSIAN MIXTURE MODELS FOR CONVERSATIONAL TELEPHONE SPEECH RECOGNITION

被引：0

作者：

Ma, Zejun ^{[1
]}

Wang, Xiaorui ^{[1
]}

Xu, Bo ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Digital Content Technol Res Ctr, Beijing 100190, Peoples R China

[2] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2012年

关键词：

Speech recognition with low resources; unsupervised learning; subspace acoustic model; BROADCAST NEWS;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper presents our preliminary works on exploring unsupervised training of subspace gaussian mixture models for under-resourced CTS recognition task. The subspace model yields better performance than conventional GMM model, particularly in small or middle-sized training set. As an effective way to save human efforts, unsupervised learning is often applied to automatically transcribe a large amount of speech archives. The additional auto-transcribed data may help to improve model accuracy. In this paper, experiments are carried out on two publicly available English conversational telephone speech corpora. Both GMM and SGMM model in combination with unsupervised learning are examined and compared in this paper.

引用

页码：4829 / 4832

页数：4

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