Sequential noise estimation with optimal forgetting for robust speech recognition

Mismatch is known to degrade the performance of speech recognition systems. In real life applications mismatch is usually nonstationary, and a general way to compensate for slowly time varying mismatch is by using sequential algorithms with forgetting. The choice of forgetting factor is usually performed empirically on some development data, and no optimality criterion is used. We introduce a framework for obtaining the optimal forgetting factor. The proposed method is applied in conjunction with a sequential noise estimation algorithm, but can be extended to sequential bias or affine transformation estimation. Speech recognition experiments conducted first under a controlled scenario on the 5K Wall Street Journal task corrupted by different noise types, then under a real-life scenario on speech recorded in a noisy car environment validate the proposed method