Novel pseudo-divergence of Gaussian mixture models based speaker clustering method

摘要

Serial structure is applied to speaker recognition to reduce the algorithm delay and computational complexity.The speech is first classified into speaker class,and then searches the most likely one inside the class.Difference between Gaussian Mixture Models(GMMs) is widely applied in speaker classification.The paper proposes a novel mean of pseudo-divergence,the ratio of Inter-Model dispersion to Intra-Model dispersion,to present the difference between GMMs,to perform speaker cluster.Weight,mean and variance,GMM’s components,are involved in the dispersion.Experiments indicate that the measurement can well present the difference of GMMs and has improved performance of speaker clustering.