This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...

Since the conventional HMM (Hidden Markov Model)-based speech enhancement methods try to improve speech quality by considering all states for the state transition, hence introduce huge computational loads inappropriate to real-time implementation. In the Left-Right HMM (LR-HMM), only the current and the next states are considered for a possible state transition so to reduce the computation comp...

A novel formulation of the nonstationary-state hidden Markov model (NS-HMM), employed as the speech model and serving as the theoretical basis for the construction of a speech enhancement system, is presented in this paper. The NS-HMM is used as a compact, parametric model, generalized from the stationary-state HMM, for describing clean speech statistics in the construction of the minimum mean-...

For a hidden Markov model (HMM) based speech recognition system it is desirable to combine enhancement of the acoustical signal and statistical representation of model parameters , ensuring both a high quality speech signal and an appropriately trained HMM. In this paper the incre-mental variant of maximum a posteriori (MAP) estimation is used to adjust the parameters of a talker-independent HM...

Hidden Markov model (HMM) based speech synthesis has a tendency to over-smooth the spectral envelope of speech, which makes the speech sound muffled. One means to compensate for the over-smoothing is to enhance the formants of the spectral model. This paper compares the performance of different formant enhancement methods, and studies the enhancement of the formants prior to HMM training in ord...

Widely Speech Signal Processing has not been used much in the field of electronics and computers due to the complexity and variety of speech signals and sounds with the advent of new technology. However, with modern processes, algorithms, and methods which can proc Demand for speech recognition technology is expected to their mobile phones as all purpose lifestyle devices. In this paper, an imp...

An extension of the HMM-based speech enhancement approach [1] is presented. The HMM-based scheme uses hidden Markov models (HMM) to control a state-dependent Wiener filter, which is used to process the noisy speech signal. This scheme gives enhanced speech signals without the annoying tonal artefacts (‘musical noise’) of the spectral subtraction approach. However, parts of the enhanced signal o...

An improved hidden Markov model-based (HMMbased) speech enhancement system designed using the minimum mean square error principle is implemented and compared with a conventional spectral subtraction system. The improvements to the system are: 1) incorporation of mixture components in the HMM for noise in order to handle noise nonstationarity in a more flexible manner, 2) two efficient methods i...