Maximum-likelihood compensation of zero-memory nonlinearities in speech signals

An algorithm to blindly compensate zero-memory nonlinear distortions of speech waveforms is derived and analyzed. This method finds a maximum-likelihood estimate of the distortion without a priori knowledge of the microphone characteristics by using the expectation-maximization algorithm. The autoregressive signal model coefficients are solved jointly with the nonlinearity estimate created by an extended Kalman filter. Also, a new family of nonlinear functions is developed for use with this algorithm, although the method can estimate the shape of any parametric zero-memory nonlinearity. These nonlinear distortions can degrade the speech recognition rates, yet lower the perceptual quality only slightly. The compensation algorithm improves automatic speech recognition of distorted speech for a variety of such nonlinearities