Unsupervised Speaker Indexing Using Generic Models

Unsupervised speaker indexing sequentially detects points where a speaker identity changes in a multispeaker audio stream, and categorizes each speaker segment, without any prior knowledge about the speakers. This paper addresses two challenges: The first relates to sequential speaker change detection. The second relates to speaker modeling in light of the fact that the number/identity of the speakers is unknown. To address this issue, a predetermined generic speaker-independent model set, called the sample speaker models (SSM), is proposed. This set can be useful for more accurate speaker modeling and clustering without requiring training models on target speaker data. Once a speaker-independent model is selected from the generic sample models, it is progressively adapted into a specific speaker-dependent model. Experiments were performed with data from the Speaker Recognition Benchmark NIST Speech corpus (1999) and the HUB-4 Broadcast News Evaluation English Test material (1999). Results showed that our new technique, sampled using the Markov Chain Monte Carlo method, gave 92.5% indexing accuracy on two speaker telephone conversations, 89.6% on four-speaker conversations with the telephone speech quality, and 87.2% on broadcast news. The SSMs outperformed the universal background model by up to 29.4% and the universal gender models by up to 22.5% in indexing accuracy in the experiments of this paper.