A GMM-based robust incremental adaptation with a forgetting factor for speaker verification
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
A review on speaker diarization systems and approaches
Speech Communication
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This paper addresses a novel algorithm of incremental speaker adaptation (ISA) based on universal background model (UBM) for saving storage and real-time processing. This algorithm can be seen as an extension of traditional speaker adaptation. It consists of two steps, adaptation and combination. It not only considers the speaker's characteristics in limited training data, but also prohibits over-fitting of the updated model. The incremental adaptation algorithm needs little storage and meets the requirement of real-time processing. In order to evaluate the efficiency and effectivity of the proposed approach, a real-time speaker segmentation system for broadcasting news is built. Experiment results demonstrate that our approach yields real time operation and achieves satisfactory performance.