On Maximizing the Within-Cluster Homogeneity of Speaker Voice Characteristics For Speech Utterance Clustering
Wei-Ho Tsai, Hsin‐Min Wang · 2006
This paper investigates the problem of how to partition unknown speech utterances into clusters, such that the overall within-cluster homogeneity of speakers' voice characteristics can be maximized. The within-cluster homogeneity is characterized by the likelihood probability that a cluster model, trained using all the utterances within a cluster, matches each of the within-cluster utterances. Such probability is then maximized by using a genetic algorithm, which determines the best cluster where each utterance should be located. For greater computational efficiency, also proposed is an alternative solution that approximates the likelihood probability with a divergence-based model similarity. The method is further designed to estimate the optimal number of clusters automatically.