Noise-robust speech recognition using top-down selective attention with an HMM classifier

Abstract

For noise-robust speech recognition, we incorporated a top-down attention mechanism into a hidden Markov model classifier with Mel-frequency cepstral coefficient features. The attention filter was introduced at the outputs of the Mel-scale filterbank and adjusted to maximize the log-likelihood of the attended features with the attended class. A low-complexity constraint was proposed to prevent the attention filter from over-fitting' and a confidence measure was introduced on the attention. A classification was made to the class with the maximum confidence measure, and demonstrated 54 % and 68 % reduction of the false recognition rate with 15-and 20-dB signal-to-noise ratio, respectively.