Fourier model based features for analysis and classification of out-of-breath speech

This paper presents a new method of feature extraction using Fourier model for analysis of out-of-breath speech. The proposed feature is evaluated using mutual information (MI) on the difference and ratio values of the Fourier parameters, amplitude and frequency. The difference and ratio are calculated between two contiguous values of the Fourier parameters. To analyze the out-of-breath speech, a new stressed speech database, named out-of-breath speech (OBS) database, is created. The database contains three classes of speech, out-of-breath speech, low out-of-breath speech and normal speech. The effectiveness of the proposed features is evaluated with the statistical analysis. The proposed features not only differentiate the normal speech and the out-of-breath speech, but also can discriminate different breath emission levels of speech. Hidden Markov model (HMM) and support vector machine (SVM) are used to evaluate the performance of the proposed features using the OBS database. For multi-class classification problem, SVM classifier is used with binary cascade approach. The performance of the proposed features is compared with the breathiness feature, the mel frequency cepstral coefficient (MFCC) feature and the Teager energy operator (TEO) based critical band TEO autocorrelation envelope (TEO-CB-Auto-Env) feature. The proposed feature outperforms the breathiness feature, the MFCC feature and the TEO-CB-Auto-Env feature.