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 [BibTeX] [Marc21]
Sparse Hidden Markov Models for Exemplar-based Speech Recognition Using Deep Neural Network Posterior Features
Type of publication: Idiap-RR
Citation: Dighe_Idiap-RR-19-2016
Number: Idiap-RR-19-2016
Year: 2016
Month: 8
Institution: Idiap
Abstract: Statistical speech recognition has been cast as a natural realization of the compressive sensing problem in this work. The compressed acoustic observations are sub-word posterior probabilities obtained from a deep neural network. Dictionary learning and sparse recovery are exploited for inference of the high-dimensional sparse word posterior probabilities. This formulation amounts to realization of a \textit{sparse} hidden Markov model where each state is characterized by a dictionary learned from training exemplars and the emission probabilities are obtained from sparse representations of test exemplars. This new dictionary-based speech processing paradigm alleviates the need for a huge collection of exemplars as required in the conventional exemplar-based methods. We study the performance of the proposed approach for continuous speech recognition using Phonebook and Numbers'95 database.
Projects Idiap
PHASER 200021-153507
Authors Dighe, Pranay
Asaei, Afsaneh
Bourlard, Hervé
Added by: [ADM]
Total mark: 0
  • Dighe_Idiap-RR-19-2016.pdf (MD5: 698d4887ec875695a165b1d84c9b2a5a)