A novel breathiness feature for analysis and classification of speech under stress

This work explores the effect of breathiness component on speech under stress. The breathiness component in a speech signal can be estimated using different features such as period perturbation quotient (PPQ), amplitude perturbation quotient (APQ), harmonic to noise ratio (HNR), glottal to noise excitation ratio (GNER), harmonic energy (HE), harmonic energy of residue (HER) and harmonic to signal ratio (HSR). Statistical analysis of these features shows that they have different mean and variance values for speech under stress. The performance of breathiness features is evaluated using Hidden Markov Model (HMM) for classification of speech under stress. The results show that the breathiness features successfully characterize the speech under stress. The performance of breathiness features is compared with the MFCC feature. Finally, a speech under stress classification method is proposed with the combination of breathiness and MFCC features. In terms of classification rates, the proposed combined feature outperforms the MFCC feature.