Abstract
Exemplar-based speech enhancement systems work by decomposing the noisy speech as a weighted sum of speech and noise exemplars stored in a dictionary and use the resulting speech and noise estimates to obtain a time-varying filter in the full-resolution frequency domain to enhance the noisy speech. To obtain the decomposition, exemplars sampled in lower dimensional spaces are preferred over the full-resolution frequency domain for their reduced computational complexity and the ability to better generalize to unseen cases. But the resulting filter may be sub-optimal as the mapping of the obtained speech and noise estimates to the full-resolution frequency domain yields a low-rank approximation. This paper proposes an efficient way to directly compute the full-resolution frequency estimates of speech and noise using coupled dictionaries: an input dictionary containing atoms from the desired exemplar space to obtain the decomposition and a coupled output dictionary containing exemplars from the full-resolution frequency domain. We also introduce modulation spectrogram features for the exemplar-based tasks using this approach. The proposed system was evaluated for various choices of input exemplars and yielded improved speech enhancement performances on the AURORA-2 and AURORA-4 databases. We further show that the proposed approach also results in improved word error rates (WERs) for the speech recognition tasks using HMM-GMM and deep-neural network (DNN) based systems.
Original language | English |
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Pages (from-to) | 1788-1799 |
Number of pages | 12 |
Journal | Ieee-Acm transactions on audio speech and language processing |
Volume | 23 |
Issue number | 11 |
DOIs | |
Publication status | Published - 1 Nov 2015 |
Publication type | A1 Journal article-refereed |
Keywords
- Exemplar-based
- Modulation envelope
- Noise robust automatic speech recognition
- Non-negative sparse coding
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Media Technology
- Acoustics and Ultrasonics
- Instrumentation
- Linguistics and Language
- Speech and Hearing