A Data-Driven Model Parameter Compensation Method for Noise-Robust Speech Recognition

Yongjoo CHUNG  

IEICE TRANSACTIONS on Information and Systems   Vol.E88-D   No.3   pp.432-434
Publication Date: 2005/03/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.3.432
Print ISSN: 0916-8532
Type of Manuscript: Special Section LETTER (Special Section on Corpus-Based Speech Technologies)
noisy speech recognition,  HMM,  

Full Text: PDF(82.1KB)
>>Buy this Article

A data-driven approach that compensates the HMM parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional methods such as the PMC, the various statistical information necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared with the PMC for the noisy speech recognition.