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Acoustic Feature Optimization for Emotion Affected Speech Recognition

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4 Author(s)
Yanqing Sun ; ThinkIT Speech Lab., Chinese Acad. of Sci., Beijing, China ; Yu Zhou ; Qingwei Zhao ; Yonghong Yan

This paper tries to deal with the problem of performance degradation in emotion affected speech recognition. The F-ratio analysis method in statistics is utilized to analyze the significance of different frequency bands for speech unit classification. The result is then used to optimize filter bank design for Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) features respectively in emotion affected speech recognition. Under comparable conditions, the modified features get a relative 40.14% decrease for MFCC and 34.93% for PLP in sentence error rate.

Published in:

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Date of Conference:

19-20 Dec. 2009