An on-line NMF model for temporal pattern learning: theory with application to automatic speech recognition

Authors:
Hugo Van Hamme
Affiliations:
Department ESAT, University of Leuven, Leuven, Belgium
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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Abstract

Convolutional non-negative matrix factorization (CNMF) can be used to discover recurring temporal (sequential) patterns in sequential vector non-negative data such as spectrograms or posteriorgrams. Drawbacks of this approach are the rigidity of the patterns and that it is intrinsically a batch method. However, in speech processing, like in many other applications, the patterns show a great deal of time warping variation and recognition should be on-line (possibly with some processing delay). Therefore, time-coded NMF (TC-NMF) is proposed as an alternative to CNMF to locate temporal patterns in time. TC-NMF is motivated by findings in neuroscience. The sequential data are first processed by a bank of filters such as leaky integrators with different time constants. The responses of these filters are modeled jointly by a constrained NMF. Algorithms for learning, decoding and locating patterns in time are proposed and verified with preliminary ASR experiments.