4,991 research outputs found
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
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 dimensions 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 enhancement
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
SEGAN: Speech Enhancement Generative Adversarial Network
Current speech enhancement techniques operate on the spectral domain and/or
exploit some higher-level feature. The majority of them tackle a limited number
of noise conditions and rely on first-order statistics. To circumvent these
issues, deep networks are being increasingly used, thanks to their ability to
learn complex functions from large example sets. In this work, we propose the
use of generative adversarial networks for speech enhancement. In contrast to
current techniques, we operate at the waveform level, training the model
end-to-end, and incorporate 28 speakers and 40 different noise conditions into
the same model, such that model parameters are shared across them. We evaluate
the proposed model using an independent, unseen test set with two speakers and
20 alternative noise conditions. The enhanced samples confirm the viability of
the proposed model, and both objective and subjective evaluations confirm the
effectiveness of it. With that, we open the exploration of generative
architectures for speech enhancement, which may progressively incorporate
further speech-centric design choices to improve their performance.Comment: 5 pages, 4 figures, accepted in INTERSPEECH 201
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