ADAPTATION TO NEW MICROPHONES USING TIED-MIXTURE NORMALIZATION

摘要

In this paper, we present several approaches designed to increase the robustness of BYBLOS, the BBN continuous speech recognition system. We address the problem of increased degradation in performance when there is mismatch in the characteristics of the training and the test microphones. We introduce a new supervised adaptation algorithm that computes a transformation from the training microphone codebook to that of a new microphone, given some information about the new microphone. Results are reported for the development and evaluation test sets of the 1993 AREA. CSR Spoke 6 WSJ task, which consist of speech recorded with two alternate microphones, a stand-mount and a telephone microphone. The proposed algorithm improves the performance of the system when tested with the stand-mount microphone by reducing the difference in error rate between the high quality training microphone and the alternate stand-mount microphone recordings by a factor of 2. Several results are presented for the telephone speech leading to important conclusions: a) the performance on telephone speech is dramatically improved by simply retraining the system on the high-quality training data after they have been bandlimited in the telephone bandwith; and b) additional training data recorded with the high quality microphone give further substantial improvement in performance.