Acoustic Feature Optimization for Emotion Affected Speech Recognition

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.