Unsupervised speaker indexing using speaker model selection based on Bayesian information criterion

The paper addresses unsupervised speaker indexing for discussion audio archives. In discussions, the speaker changes frequently, thus the duration of utterances is very short and its variation is large, which causes significant problems in applying conventional methods such as model adaptation and variance-BIC (Bayesian information criterion) methods. We propose a flexible framework that selects an optimal speaker model (GMM or VQ) based on the BIC according to the duration of utterances. When the speech segment is short, the simple and robust VQ-based method is expected to be chosen, while GMM can be reliably trained for long segments. For a discussion archive having a total duration of 10 hours, it is demonstrated that the proposed method achieves higher indexing performance than that of conventional methods.