Speech enhancement by perceptual filter with sequential noise parameter estimation

We report work on speech enhancement that combines sequential noise estimation and perceptual filtering. The sequential estimation employs an extension of the sequential EM-type algorithm. In the algorithm, statistics of clean speech are modeled by hidden Markov models (HMM) and noise is assumed to be Gaussian distributed with a time-varying mean vector (the noise parameter) to be estimated. The estimation process uses a non-linear function that relates speech statistics, noise, and noisy observation. With the estimated noise parameter, the subtraction-type algorithm for speech enhancement may be extended to non-stationary environments. In particular, a perceptual filter with frequency masking is constructed with a tradeoff between noise reduction and speech distortion considering the sensitivity of speech recognition systems to speech distortion. Our experiments in speech enhancement and speech recognition in non-stationary noise confirmed that this approach seems promising in improving performances compared to alternative speech enhancement algorithms.