Speaker identification using supra-segmental pitch pattern dynamics
Most conventional speaker identification systems rely on short-time spectral envelope features. Recent efforts have yielded significant progress by capturing and modeling speaker-specific aspects of long-term information in the spoken language signal such as prosodic, syntactic and other conversational features. Although significant results have been reported, substantial improvements can be made by using detailed models better describing the specific behavior of each feature. We focus on modeling pitch pattern dynamics at different prosodic scale levels. Trends in pitch variation are believed to appear at different time-scales - such as microprosody, accent, phrase and discourse levels - making wavelet analysis of the f0 contour a suitable choice for investigating the corresponding pitch patterns. We then introduce a transform of the f0 contour wavelet coefficients that results in a compact representation and better reveals the spatio-temporal details in the coefficient sequences representation. In turn, the dynamics of the transformed sequence are modeled by a first order Markov chain, at each scale level. Classification is carried out at each level and the scores of the classifiers operating at the different supra-segmental levels are fused together. The proposed method achieves an EER of 4.8% on the NIST 2001 speaker ID extended data task using a 16-conversation subset, based solely on f0-based information.