Variational Bayesian learning for speech modeling and enhancement
Signal Processing
Super-human multi-talker speech recognition: A graphical modeling approach
Computer Speech and Language
A computational auditory scene analysis system for speech segregation and robust speech recognition
Computer Speech and Language
A novel associative memory approach to speech enhancement in a vehicular environment
Expert Systems with Applications: An International Journal
Robust speech recognition by integrating speech separation and hypothesis testing
Speech Communication
Speech enhancement based on Sparse Code Shrinkage employing multiple speech models
Speech Communication
Speech enhancement using hidden Markov models in Mel-frequency domain
Speech Communication
Speech enhancement using generalized weighted β-order spectral amplitude estimator
Speech Communication
| Hi-index | 35.69 |
A Bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed. In particular, minimum mean square error (MMSE) and maximum a posteriori (MAP) signal estimators are developed using hidden Markov models (HMMs) for the clean signal and the noise process. It is shown that the MMSE estimator comprises a weighted sum of conditional mean estimators for the composite states of the noisy signal, where the weights equal the posterior probabilities of the composite states given the noisy signal. The estimation of several spectral functionals of the clean signal such as the sample spectrum and the complex exponential of the phase is also considered. A gain-adapted MAP estimator is developed using the expectation-maximization algorithm. The theoretical performance of the MMSE estimator is discussed, and convergence of the MAP estimator is proved. Both the MMSE and MAP estimators are tested in enhancing speech signals degraded by white Gaussian noise at input signal-to-noise ratios of from 5 to 20 dB