Mixture autoregressive hidden Markov models for speech signals

Nonlinear mixture autoregressive hidden Markov models for speech recognition

Gaussian mixture models are a very successful method for modeling the output distribution of a state in a hidden Markov model (HMM). However, this approach is limited by the assumption that the dynamics of speech features are linear and can be modeled with static features and their derivatives. In this paper, a nonlinear mixture autoregressive model is used to model state output distributions (...

On the application of hidden Markov models for enhancing noisy speech

w e ppose a new algorithm for enhancing noisy speech which have been degraded by statistically independent additive noise. The al p rithm is based upon modeling the clean speech as a hidden Markov process with mixtures of Gaussian autoregressive (AR) output processes, and the noise process as a sequence of stationary, statistically independent, Gaussian AR vectors. The parameter sets of the mod...

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

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

On nonstationary hidden Markov modeling of speech signals

We propese an exact maximum likelihood (ML) approach for hidden Markov modeling of speech signals using models with mixtures of Gaussian autoregressive (AR) output probability distributions. This approach differs from the commonly used approach in two aspects. First, the parameters of the AR models are calculated using the exact, rather than the asymptotic, form of the likelihood function. Seco...