Unsupervised discovery of recurring speech patterns using probabilistic adaptive metrics

Okko Räsänen, María Andrea Cruz Blandón

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)
30 Downloads (Pure)

Abstract

Unsupervised spoken term discovery (UTD) aims at finding recurring segments of speech from a corpus of acoustic speech data. One potential approach to this problem is to use dynamic time warping (DTW) to find well-aligning patterns from the speech data. However, automatic selection of initial candidate segments for the DTW-alignment and detection of “sufficiently good” alignments among those require some type of predefined criteria, often operationalized as threshold parameters for pair-wise distance metrics between signal representations. In the existing UTD systems, the optimal hyperparameters may differ across datasets, limiting their applicability to new corpora and truly low-resource scenarios. In this paper, we propose a novel probabilistic approach to DTW-based UTD named as PDTW. In PDTW, distributional characteristics of the processed corpus are utilized for adaptive evaluation of alignment quality, thereby enabling systematic discovery of pattern pairs that have similarity what would be expected by coincidence. We test PDTW on Zero Resource Speech Challenge 2017 datasets as a part of 2020 implementation of the challenge. The results show that the system performs consistently on all five tested languages using fixed hyperparameters, clearly outperforming the earlier DTW-based system in terms of coverage of the detected patterns.

Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherInternational Speech Communication Association
Pages4871-4875
Number of pages5
Volume2020-October
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInterspeech - Shanghai, China
Duration: 25 Oct 202029 Oct 2020
Conference number: 21
http://www.interspeech2020.org/

Publication series

NameInterspeech
PublisherInternational Speech Communication Association
ISSN (Print)2308-457X

Conference

ConferenceInterspeech
Abbreviated titleINTERSPEECH
CountryChina
CityShanghai
Period25/10/202029/10/2020
Internet address

Keywords

  • Dynamic time warping
  • Pattern matching
  • Unsupervised learning
  • Zero resource speech processing

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