Unsupervised speaker recognition based on competition between self-organizing maps

Itshak Lapidot, Hugo Guterman, Arnon Cohen

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

We present a method for clustering the speakers from unlabeled and unsegmented conversation (with known number of speakers), when no a priori knowledge about the identity of the participants is given. Each speaker was modeled by a self-organizing map (SOM). The SOMs were randomly initiated. An iterative algorithm allows the data move from one model to another and adjust the SOMs. The restriction that the data can move only in small groups but not by moving each and every feature vector separately force the SOMs to adjust to speakers (instead of phonemes or other vocal events). This method was applied to high-quality conversations with two to five participants and to two-speaker telephone-quality conversations. The results for two (both high- and telephone-quality) and three speakers were over 80% correct segmentation. The problem becomes even harder when the number of participants is also unknown. Based on the iterative clustering algorithm a validity criterion was also developed to estimate the number of speakers. In 16 out of 17 conversations of high-quality conversations between two and three participants, the estimation of the number of the participants was correct. In telephone-quality the results were poorer.

Original languageEnglish
Pages (from-to)877-887
Number of pages11
JournalIEEE Transactions on Neural Networks
Volume13
Issue number4
DOIs
StatePublished - 1 Jul 2002

Keywords

  • Competitive learning
  • Segmentation
  • Self-organizing maps (SOMs)
  • Speaker recognition
  • Temporal data clustering
  • Vector quantization (VQ)

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