A new speaker change detection method in a speaker identification system for two-speakers segmentation

Abstract

Speaker change detection is done in many speaker and speech identification applications that the speech is from two speakers. However, the standard metric-based methods performance is not suitable and stable owing to the amid window distance calculation stability. Therefore, a new method is proposed to improve the stability and enhance the performance of the system according to speakers' characteristics using between window correlations. Moreover, reference speaker models set that shows the space of the entire speaker model are trained in this approach. A metric is defined as the between window correlation of scores likelihood vectors versus the reference models. The Peak and Valley information and gender information are also used. In this paper, we look at telephone conversations where it is known a priori that there are two speakers, but the identity of the speakers is not known. Experiments over Farsdat Database show better performance In comparison with the GLR and the BIC approaches. This new approach has more effect rather than the GLR and the BIC approaches in the broader value of defined thresholds.

Development of High Accuracy Classifier for the Speaker Recognition System

“…Other methods such as zero-crossing, convolution, and correlation are commonly used as a time domain method to analyse the speech signal. Conventional voice recognition models depending on the aforementioned approaches are not consistence for overcoming the time-varying nature of voice signals [10]. From the popular acoustic feature extraction methods, LPCC is used in [11] for modelling the speech production process.…”