Hierarchical stochastic feature matching for robust speech recognition

被引：0

作者：

Jiang, H ^{[1
]}

Soong, F ^{[1
]}

Lee, CH ^{[1
]}

机构：

[1] Bell Labs, Lucent Technol, Multimedia Commun Res Lab, Dialogue Syst Res, Murray Hill, NJ 07974 USA

来源：

2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS: VOL I: SPEECH PROCESSING 1; VOL II: SPEECH PROCESSING 2 IND TECHNOL TRACK DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS NEURALNETWORKS FOR SIGNAL PROCESSING; VOL III: IMAGE & MULTIDIMENSIONAL SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING - VOL IV: SIGNAL PROCESSING FOR COMMUNICATIONS; VOL V: SIGNAL PROCESSING EDUCATION SENSOR ARRAY & MULTICHANNEL SIGNAL PROCESSING AUDIO & ELECTROACOUSTICS; VOL VI: SIGNAL PROCESSING THEORY & METHODS STUDENT FORUM | 2001年

关键词：

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this paper we investigate how to improve the robustness of a speech recognizer in a noisy, mismatched environment when only a single or a few test utterances are available for compensating the mismatch. A new hierarchical tree-based transformation is proposed to enhance the conventional stochastic matching algorithm in the cepstral feature space. The tree-based hierarchical transformation is estimated in two criteria: i) maximum likelihood (ML) using the current test utterance; ii) Sequential maximum a posterior (MAP) using the current and previous utterances. Recognition results obtained using a hands-free database show the proposed feature compensation is robust. Significant performance improvement has been observed over the conventional stochastic matching.

引用

页码：217 / 220

页数：4

共 6 条

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[6] [No title captured]

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