Nonlinear compensation for stochastic matching

The performance of an automatic speech recognizer degrades when there exist s an acoustic mismatch between the training and the testing conditions in t he data. Though it is certain that the mismatch is nonlinear, its exact for m is unknown. Tackling the problem of nonlinear mismatches is a difficult t ask that has not been adequately addressed before. In this paper, we develo p an approach that uses nonlinear transformations in the stochastic matchin g framework to compensate for acoustic mismatches, The functional form of t he nonlinear transformation is modeled by neural networks. We develop a new technique to train neural networks using the generalized EM algorithm. Thi s technique eliminates the need for stereo databases, which are difficult t o obtain in practical applications. The new technique is data-driven and he nce can be used under a wide variety of conditions without a priori knowled ge of the environment, Using this technique, we show that we can provide im provement under various types of acoustic mismatch; in some cases a 72% red uction in word error rate is achieved.