Noise robust exemplar matching with alpha–beta divergence

© 2015 Elsevier B.V. All rights reserved. The noise robust exemplar matching (N-REM) framework performs automatic speech recognition using exemplars, which are the labeled spectrographic representations of speech segments extracted from training data. By incorporating a sparse representations formulation, this technique remedies the inherent noise modeling problem of conventional exemplar matching-based automatic speech recognition systems. In this framework, noisy speech segments are approximated as a sparse linear combination of the exemplars of multiple lengths, each associated with a single speech unit such as words, half-words or phones. On account of the reconstruction error-based back end, the recognition accuracy highly depends on the congruence of the speech features and the divergence metric used to compare the speech segments with exemplars. In this work, we replace the conventional Kullback-Leibler divergence (KLD) with a generalized divergence family called the Alpha-Beta divergence with two parameters, α and β, in conjunction with mel-scaled magnitude spectral features. The proposed recognizer traverses the (α,β) plane depending on the amount of contamination to provide better separation of speech and noise sources. Moreover, we apply our recently proposed active noise exemplar selection (ANES) technique in a more realistic scenario where the target utterances are degraded by genuine room noise. Recognition experiments on the small vocabulary track of the 2nd CHiME Challenge and the AURORA-2 database have shown that the novel recognizer with the AB divergence and ANES outperforms the baseline system using the generalized KLD with tuned sparsity, especially at lower SNR levels.Yilmaz E., Gemmeke J.F., Van hamme H., ''Noise robust exemplar matching with alpha–beta divergence'', Speech communication, vol. 76, pp. 127-142, February 2016.status: publishe