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

David Nesbitt; Danny Crookes; Ji Ming

ICASSP 2018accepted0 citations

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

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×.

BibTeX

@inproceedings{icassp2018_speechsegmentclu,
  title = {Speech Segment Clustering for Real-Time Exemplar-Based Speech Enhancement},
  author = {David Nesbitt and Danny Crookes and Ji Ming},
  booktitle = {ICASSP 2018},
  year = {2018}
}