Sciweavers

Share
SPEECH
2016

Sparse modeling of neural network posterior probabilities for exemplar-based speech recognition

3 years 12 months ago
Sparse modeling of neural network posterior probabilities for exemplar-based speech recognition
In this paper, a compressive sensing (CS) perspective to exemplar-based speech processing is proposed. Relying on an analytical relationship between CS formulation and statistical speech recognition (Hidden Markov Models - HMM), the automatic speech recognition (ASR) problem is cast as recovery of highdimensional sparse word representation from the observed low-dimensional acoustic features. The acoustic features are exemplars obtained from (deep) neural network sub-word conditional posterior probabilities. Low-dimensional word manifolds are learned using these sub-word posterior exemplars and exploited to construct a linguistic dictionary for sparse representation of word posteriors. Dictionary learning has been found to be a principled way to alleviate the need of having huge collection of exemplars as required in conventional exemplar-based approaches, while still improving the performance. Context appending and collaborative hierarchical sparsity are used to exploit the sequential...
Pranay Dighe, Afsaneh Asaei, Hervé Bourlard
Added 10 Apr 2016
Updated 10 Apr 2016
Type Journal
Year 2016
Where SPEECH
Authors Pranay Dighe, Afsaneh Asaei, Hervé Bourlard
Comments (0)
books