Speech Segment Clustering for Real-Time Exemplar-Based Speech Enhancement

David Nesbitt, Daniel Crookes, Ming Ji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
212 Downloads (Pure)

Abstract

Exemplar-based (or Corpus-based) speech enhancement algorithms have great potential but are typically slow due to needing to search through the entire corpus. The properties of speech can be exploited to improve these algorithms. Firstly, a corpus can be clustered by a phonetic ordering into a search tree which can be used to find a best matching segment. This dramatically reduces the search space, reducing the time complexity of searching a corpus of n segments from O(n) to O(log(n)). Secondly, clustering can be used to give a lossy compression of a speech corpus by replacing original segments with codewords. These techniques are shown in comparison with sequential search and non-compressed corpora using a simple speech enhancement algorithm. A combination of these techniques for a corpus of a quarter of WSJO results in a speedup of approximately 3000×.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5419 - 5423
ISBN (Electronic)978-1-5386-4658-8
DOIs
Publication statusPublished - 13 Sep 2018

Publication series

Name
ISSN (Electronic)2379-190X

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  • Student Theses

    Speech Enhancement for Real-Time Applications

    Author: Nesbitt, D., Jul 2020

    Supervisor: Ji, M. (Supervisor), Crookes, D. (Supervisor) & McLaughlin, N. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy

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    Cite this

    Nesbitt, D., Crookes, D., & Ji, M. (2018). Speech Segment Clustering for Real-Time Exemplar-Based Speech Enhancement. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5419 - 5423). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.2018.8461689