One of the largest challenges in speaker recognition is dealing with speaker-emotion variability problem. Nowadays, compensation techniques are the main solutions to this problem. In these methods, all kinds of speakers' emotion speech should be elicited thus it is not user-friendly in the application. Therefore the basic problem is how to get the distribution of speakers' emotion speech and how to train emotion GMM from their natural speech. This paper presents a natural-emotion GMM transformation algorithm to train users' emotion model to overcome this problem. The algorithm can convert natural GMM to emotion GMM based on an emotion database. It only needs speakers' natural speech and needn't to align the natural utterances with the emotion utterances. The performance evaluation is carried on the MASC database. The promising result is achieved compared to the traditional speaker verification.
Bibliographic reference. Shan, Zhenyu / Yang, Yingchun / Ye, Ruizhi (2007): "Natural-emotion GMM transformation algorithm for emotional speaker recognition", In INTERSPEECH-2007, 782-785.