An hypothesized Wiener filtering approach to noisy speech recognition

The problem of speech recognition in a noisy environment is addressed. The mismatch problem originated when training a system in a clean environment and operating it in a noisy one. When measuring the similarity between a noisy test utterance and a list of clean templates a correction process, based on a series of Wiener filters built using the hypothesized clean template, is applied to the feature vectors of the noisy word. The filtering process is optimized as a by-product of the dynamic programming algorithm of the scoring step. Tests were conducted on two databases, one in Hebrew and the second in Japanese, using additive white and car noise at different SNRs. The method shows a good performance and compares well with other methods proposed in the literature.