Spectral slope based analysis and classification of stressed speech

Abstract

In this study, spectral slope based features are investigated for characterization and classification of stressed speech. The vocal tract spectrum is modulated with glottal flow spectra, resulting a tilt in the overall spectrum. In this study, spectral tilt is analyzed for different stress classes. Relative formant peak displacement (RFD) is proposed as the displacement of formant peaks from the 1 st formant peak. The displacement of 2 nd , 3 rd and 4 th formant peaks from 1 st formant peak is termed as RFD 2, RFD 3 and RFD 4, respectively. The features are extracted from linear prediction coefficient (LPC) and cepstrally smoothed log spectrum, respectively. Analysis shows that stress effects higher formant region more than lower formant region. To evaluate the effectiveness of this feature for different stress classes, the performance of stress classification is evaluated. A simulated stressed speech database is collected under four stress conditions, namely, neutral, angry, sad and Lombard from fifteen speakers. The performance of RFD feature is similar to Mel-frequency cepstral coefficient (MFCC). This shows that RFD feature have approximately same discrimination capability for stress as MFCC. Further, the performance of cepstrally smoothed log spectra derived RFD are higher than LPC derived RFD feature. RFD features are combined with MFCC in feature, score and rank level and found improved performance.