Cepstral Compensation Using Statistical Linearization

Speech recognition systems perform poorly on speech degraded by even simple effects such as linear filtering and additive noise. One solution to this problem is to modify the probability density function (PDF) of clean speech to account for the effects of the degradation. However, even for the case of linear filtering and additive noise, it is extremely difficult to do this analytically. Previously-attempted analytical solutions for the problem of noisy speech recognition have either used an overly-simplified mathematical description of the effects of noise on the statistics of speech, or they have relied on the availability of large environment-specific adaptation sets. In this paper we present the Vector Polynomial approximationS (VPS) method to compensate for the effects of linear filtering and additive noise on the PDF of clean speech. VPS also estimates the parameters of the environment, namely the noise and the channel, by using statistically linearized approximations of these effects. We evaluate the performance of this method (VPS) using the CMU SPHINX-II system on the alphanumeric CENSUS database corrupted with artificial white Gaussian noise. VPS provides improvements of up to 15 percent in relative recognition accuracy over our previous best algorithm, VTS, while being up to 20 percent more computationally efficient.