Clustering short push-to-talk segments

Ilya Shapiro; Neta Rabin; Irit Opher; Itshak Lapidot

Clustering short push-to-talk segments

Ilya Shapiro, Neta Rabin, Irit Opher, Itshak Lapidot

Afeka Tel Aviv Academic College of Engineering

Research output: Contribution to journal › Conference article › peer-review

10 Scopus citations

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 language	English
Pages (from-to)	3031-3035
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume	2015-January
State	Published - 2015
Externally published	Yes
Event	16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany Duration: 6 Sep 2015 → 10 Sep 2015

Keywords

Cosine distance
Mean-shift clustering
Short segments
Speaker clustering

Cite this

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title = "Clustering short push-to-talk segments",

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.",

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AU - Rabin, Neta

AU - Opher, Irit

AU - Lapidot, Itshak

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AB - 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.

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JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

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Y2 - 6 September 2015 through 10 September 2015

ER -

Clustering short push-to-talk segments

Abstract

Keywords

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