An analysis of nonstationary variance estimates in the maximum negentropy beamformer

This work extends a beamforming algorithm intended for automatic recognition of speech data captured with an array of distant microphones. In addition to enforcing a distortionless constraint in a desired direction, the algorithm adjusts the sensor weights so as to maximize a negentropy criterion. Negentropy is a measure of how non-Gaussian the probability density function (pdf) of a random variable is, and thus its computation depends on a number of pdf parameters. Here time-dependent pdf parameters are introduced to account for the nonstationarity of speech. Several methods are evaluated in a set of far-field ASR experiments. It is found that phone-length windows for the estimation lead to an increase of word error rate, and an analysis is provided that clarifies the reason for this behavior. Most importantly, we provide evidence that negentropy may not be an ideal cost criterion, not only when using phone-dependent parameters, but also in the original system.