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Exemplar-based speech enhancement for deep neural network based automatic speech recognition

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Details

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
Pages4485-4489
Number of pages5
ISBN (Print)9781467369978
DOIs
Publication statusPublished - 4 Aug 2015
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech and Signal Processing -
Duration: 1 Jan 19001 Jan 2000

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Period1/01/001/01/00

Abstract

Deep neural network (DNN) based acoustic modelling has been successfully used for a variety of automatic speech recognition (ASR) tasks, thanks to its ability to learn higher-level information using multiple hidden layers. This paper investigates the recently proposed exemplar-based speech enhancement technique using coupled dictionaries as a pre-processing stage for DNN-based systems. In this setting, the noisy speech is decomposed as a weighted sum of atoms in an input dictionary containing exemplars sampled from a domain of choice, and the resulting weights are applied to a coupled output dictionary containing exemplars sampled in the short-time Fourier transform (STFT) domain to directly obtain the speech and noise estimates for speech enhancement. In this work, settings using input dictionary of exemplars sampled from the STFT, Mel-integrated magnitude STFT and modulation envelope spectra are evaluated. Experiments performed on the AURORA-4 database revealed that these pre-processing stages can improve the performance of the DNN-HMM-based ASR systems with both clean and multi-condition training.

Keywords

  • coupled dictionaries, deep neural networks, modulation envelope, non-negative matrix factorisation, speech enhancement

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