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Noise-Robust Speech Recognition Using Top-Down Selective Attention With an HMM Classifier

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.