Unsupervised Speaker Change Detection using Probabilistic Pattern Matching

A. Malegaonkar, A. Ariyaeeinia, P. Sivakumaran, J. Fortuna

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
45 Downloads (Pure)


This letter presents an investigation into the use of a probabilistic pattern matching approach for detecting speaker changes in audio streams. The experiments are conducted using clean speech as well as broadcast news material. It is shown that, in the proposed approach, the use of bilateral scoring is considerably more effective than unilateral scoring. Appropriate score normalization methods are considered in the study. It is observed that in all the cases, the bilateral scoring approach outperforms the currently popular method of Bayesian information criterion (BIC) for speaker change detection. This letter discusses the principles of the proposed approach and details the experimental investigations.
Original languageEnglish
Pages (from-to)509-512
JournalIEEE Signal Processing Letters
Issue number8
Publication statusPublished - 2006


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