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http://dx.doi.org/10.9717/kmms.2013.16.8.905

Recognition Performance Improvement for Noisy-speech by Parallel Model Compensation Adaptation Using Frequency-variant added with ML  

Choi, Sook-Nam (영남대학교 정보통신공학과)
Chung, Hyun-Yeol (영남대학교 정보통신공학과)
Publication Information
Journal of Korea Multimedia Society / v.16, no.8, 2013 , pp. 905-913 More about this Journal
Abstract
The Parallel Model Compensation Using Frequency-variant: FV-PMC for noise-robust speech recognition is a method to classify the noises, which are expected to be intermixed with input speech when recognized, into several groups of noises by setting average frequency variant as a threshold value; and to recognize the noises depending on the classified groups. This demonstrates the excellent performance considering noisy speech categorized as good using the standard threshold value. However, it also holds a problem to decrease the average speech recognition rate with regard to unclassified noisy speech, for it conducts the process of speech recognition, combined with noiseless model as in the existing PMC. To solve this problem, this paper suggests a enhanced method of recognition to prevent the unclassified through improving the extent of rating scales with use of maximum likelihood so that the noise groups, including input noisy speech, can be classified into more specific groups, which leads to improvement of the recognition rate. The findings from recognition experiments using Aurora 2.0 database showed the improved results compared with those from the method of the previous FV-PMC.
Keywords
PMC; MLE; Frequency-variant; Speech Recognition;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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