Clustering short push-to-talk segments

Ilya Shapiro, Neta Rabin, Irit Opher, Itshak Lapidot

Research output: Contribution to journalConference articlepeer-review

Abstract

We present a method for clustering short push-to-talk speech segments in the presence of different numbers of speakers. Iterative Mean Shift algorithm based on the cosine distance is used to perform speaker clustering on i-vectors generated from many short speech segments. We report results as measured by the Accuracy, the average number of detected speakers (ANDS), the average cluster purity (ACP), the average speaker purity (ASP) and K . We achieve clustering accuracy of: 90.0%, 86.9% and 72.1% for 3, 15 and 60 speakers respectively.

Original languageEnglish
Pages (from-to)3031-3035
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2015-January
StatePublished - 2015
Externally publishedYes
Event16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany
Duration: 6 Sep 201510 Sep 2015

Keywords

  • Cosine distance
  • Mean-shift clustering
  • Short segments
  • Speaker clustering

Fingerprint

Dive into the research topics of 'Clustering short push-to-talk segments'. Together they form a unique fingerprint.

Cite this