Speaker adaptation experiments using nonstationary-state hidden Markov models: a MAP approach

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...

A Bayesian Prediction Approach to Robust Speech Recognition and Online Speaker Adaptation

Because the acoustic environments are uncertain and nonstationary, it is necessary to characterize the uncertainty of speech hidden Markov models (HMM’s) for recognition and trace the uncertainty sequentially to match the nonstationary environments. In this study, we develop a new Bayesian predictive classification (BPC) framework for robust decision and online speaker adaptation. The BPC decis...

Speech recognition in non-stationary adverse environments

A bshctIn this paper, we introduce a new approach, called nonstationary adaptation (NA), to recognize speech under nonstationary adverse environments. Two models are used: one is a speaker-independent hidden Markov model (HMM) for clean speech, the other is an ergodic Markov chain representing the nonstationary adverse environment. Each state in the Markov chain represents one stationary advers...

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

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