Objectively measured descriptors applied to speaker characterization

Automatic speaker identification and verification systems can achieve impressive performance levels but the determination of what physical features of the speech waveform contribute to the perceptual characterization of speakers remains a topic of research interest. We present and investigate a variety of features objectively extracted from the speech waveform to characterize perceptual speaker differences. The features belong to three broad classes of glottal, vocal tract and prosodic measurements. They are first evaluated in the context of a speaker distinguishability task by two measures: (i) an invariant criterion of cluster scattering, and, (ii) the error rate achieved by a maximum likelihood same-or-different-speaker classification. Principal components analysis indicates an underlying dimensionality of 6 out of the 20 objective descriptors.