Abstract

The estimation of the vocal tract transfer function (VTTF) for a speech signal is an essential problem in speech processing. Because the speech signal results from a convolution of the VTTF and a quasi-periodic excitation signal, there are many missing frequency components between adjacent harmonics of the fundamental frequency, which make it indeed hard to extract the accurate VTTF . To address this problem, I propose a statistical approach to the offline VTTF estimation based on a factor analyzed trajectory hidden Markov model that effectively models harmonic components observed over an utterance. This model is trained so that its likelihood for the observed harmonic component sequences is maximized while considering VTTF parameters as hidden variables. The trained model enables the maximum a posteriori (MAP) estimation of a time-varying VTTF sequence considering not only harmonic components at each analyzed frame but also those at other frames to interpolate the missing frequency components in a probabilistic manner. The effectiveness of the proposed method is demonstrated by a result of a simulation experiment.