Speaker-independent phonetic classification using hidden Markov models with mixtures of trend functions

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Temporal control and training selection for HMM-based system

Most speaker-independent acoustic-phonetic decoding systems are based on hidden Markov models. Such systems lack a real temporal control for the phonetic models. Furthermore, inter-speaker variability makes speaker adaptation necessary. In order to solve these problems, we introduce two original approaches. On the one hand, discontinuities detected with the ForwardBackward Divergence method are...

A maximum a posteriori approach to speaker adaptation using the trended hidden Markov model

A formulation of the maximum a posteriori (MAP) approach to speaker adaptation is presented with use of the trended or nonstationary-state hidden Markov model (HMM), where the Gaussian means in each HMM state are characterized by time-varying polynomial trend functions of the state sojourn time. Assuming uncorrelatedness among the polynomial coefficients in the trend functions, we have obtained...

User-customized Password Speaker Verif Gmm Model

In this paper, we present a new approach towards user-customized password speaker verification combining the advantages of hybrid HMM/ANN systems, usingArtificial Neural Networks (ANN) to estimate emission probabilities of Hidden Markov Models , and Gaussian Mixture Models. In the approach presented here, we indeed exploit the properties of hybrid HMM/ANN systems, usually resulting in high phon...