SINGER IDENTIFICATION AND TRANSFORMATION THROUGH DYNAMIC MODELING OF VOCAL FOLD AND VOCAL TRACT PARAMETERS

Oftentimes when we listen to a familiar singer, the unique qual-ities of that performer’s voice allow us to establish the singer’s identity with relative ease. It is believed that the unique acoustic qualities of an individual singer’s voice arise from a combination of innate physical factors (e.g. vocal tract and vocal fold physiol-ogy) and individual characteristics of performance and expression (e.g. pronunciation and accent). In this research, we jointly esti-mate pole-zero filter and LF glottal waveform model parameters to model the shape of the vocal tract and the glottal excitation, respectively over short time periods. These time-varying parame-ters, corresponding to the physical characteristics of the singer, are used to train a Hidden Markov Model (HMM). The HMM is used to model the dynamic behavior of the source-filter parameters, cor-responding to some of the expressive characteristics of the singer. We propose a system that is able to identify singers based upon the model of greatest likelihood among the individually trained HMMs. We also explore the use of individual HMM states as a method of mapping from the parameters of one singer to another in a preliminary attempt at singing voice transformation. The data used in this analysis was recorded from four conservatory-level classically trained singers. 1