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 [BibTeX] [Marc21]
Low-Rank Representation of Nearest Neighbor Phone Posterior Probabilities to Enhance DNN Acoustic Modeling
Type of publication: Idiap-RR
Citation: Luyet_Idiap-RR-04-2016
Number: Idiap-RR-04-2016
Year: 2016
Month: 3
Institution: Idiap
Abstract: We hypothesize that optimal deep neural networks (DNN) class-conditional posterior probabilities live in a union of low-dimensional subspaces. In real test conditions, DNN posteriors encode uncertainties which can be regarded as a superposition of unstructured sparse noise to the optimal posteriors. We aim to investigate different ways to structure the DNN outputs exploiting low-rank representation (LRR) techniques. Using a large number of training posterior vectors, the underlying low-dimensional subspace is identified through nearest neighbor analysis, and low-rank decomposition enables separation of the ``optimal'' posteriors from the spurious uncertainties at the DNN output. Experiments demonstrate that by processing subsets of posteriors which possess strong subspace similarity, low-rank representation enables enhancement of posterior probabilities, and lead to higher speech recognition accuracy based on the hybrid DNN-hidden Markov model (HMM) system.
Keywords: automatic speech recognition (ASR), Deep neural network (DNN) , k-nearest neighbor (kNN) search , low-rank representation (LRR) , posterior probability
Projects Idiap
Authors Luyet, Gil
Dighe, Pranay
Asaei, Afsaneh
Bourlard, Hervé
Crossref by Luyet_INTERSPEECH_2016
Added by: [ADM]
Total mark: 0
  • Luyet_Idiap-RR-04-2016.pdf (MD5: 4d8b493594c656077505d25a1352d19d)