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 [BibTeX] [Marc21]
Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition
Type of publication: Conference paper
Citation: Dighe_ICASSP_2016
Booktitle: Proceedings of 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016)
Year: 2016
Month: March
Pages: 5690-5694
Publisher: IEEE
Location: Shanghai
Abstract: We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low- dimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse representation of the test posteriors using this dictionary enables projection to the space of training data. Relying on the fact that the intrinsic di- mensions of the posterior subspaces are indeed very small and the matrix of all posteriors belonging to a class has a very low rank, we demonstrate how low-dimensional structures enable further en- hancement of the posteriors and rectify the spurious errors due to mismatch conditions. The enhanced acoustic modeling method leads to improvements in continuous speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in both clean and noisy conditions, where upto 15.4% relative reduction in word error rate (WER) is achieved.
Projects Idiap
PHASER 200021-153507
Authors Dighe, Pranay
Luyet, Gil
Asaei, Afsaneh
Bourlard, Hervé
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  • Dighe_ICASSP_2016.pdf