Speech recognition and enhancement by a nonstationary AR HMM with gain adaptation under unknown noise
نویسندگان
چکیده
In this paper, a gain-adapted speech recognition in unknown noise is developed in time domain. The noise is assumed to be the colored noise. The nonstationary autoregressive (NAR) hidden markov model (HMM) used to model clean speeches, The nonstationary AR is modeled by polynomial functions with a linear combination of A4 known basis functions. Enhancement using multiple Kalman filters is performed for the gain contour of speech and estimation of noise model when only the noisy signal is available.
منابع مشابه
Speech enhancement based on hidden Markov model using sparse code shrinkage
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 ...
متن کاملHMM-based strategies for enhancement of speech signals embedded in nonstationary noise
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...
متن کاملRobust speech recognition via modeling spectral coefficients with HMM's with complex Gaussian components
Robust speech recognition via hidden Markov modeling of spectral vectors is studied in this paper. The hidden Markov model (HMM) mixture components are assumed complex Gaussian with zero mean, diagonal covariance, and with incorporating an unknown scalar gain term. The gain term is associated with each spectral vector and it models the varying energy of speech signals. It is estimated by applyi...
متن کاملSpeech 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...
متن کاملNonstationary-state hidden Markov model representation of speech signals for speech enhancement
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-...
متن کامل