Statistical feature evaluation for classification of stressed speech

Abstract

The variations in speech production due to stress have an adverse affect on the performances of speech and speaker recognition algorithms. In this work, different speech features, such as Sinusoidal Frequency Features (SFF), Sinusoidal Amplitude Features (SAF), Cepstral Coefficients (CC) and Mel Frequency Cepstral Coefficients (MFCC), are evaluated to find out their relative effectiveness to represent the stressed speech. Different statistical feature evaluation techniques, such as Probability density characteristics, F-ratio test, Kolmogorov-Smirnov test (KS test) and Vector Quantization (VQ) classifier are used to assess the performances of the speech features. Four different stressed conditions, Neutral, Compassionate, Anger and Happy are tested. The stressed speech database used in this work consists of 600 stressed speech files which are recorded from 30 speakers. SAF shows maximum recognition result followed by SFF, MFCC and CC respectively with the VQ classifier. The relative classification results and the relative magnitudes of F-ratio values for SFF, MFCC and CC features are obtained with the same order. SFF and MFCC feature show consistent relative performance for all the three tests, F-ratio, K-S test and VQ classifier.