Learning State Labels for Sparse Classification of Speech with Matrix Deconvolution

Non-negative spectral factorisation with long temporal context has been successfully used for noise robust recognition of speech in multi-source environments. Sparse classification from activations of speech atoms can be employed instead of conventional GMMs to determine speech state likelihoods. For accurate classification, correct linguistic state labels must be assigned to speech atoms. We propose using non-negative matrix deconvolution for learning the labels with algorithms closely matching a framework that separates speech from additive noises. Experiments on the 1st CHiME Challenge corpus show improvement in recognition accuracy over labels acquired from original atom sources or previously used least squares regression. The new approach also circumvents numerical issues encountered in previous learning methods, and opens up possibilities for new speech basis generation algorithms.

automatic speech recognition; noise robustness; non-negative matrix factorization; sparse classification

• speech recognition