Speaker recognition method through emotional model synthesis based on neighbors preserving principle

A speaker recognition method through emotional model synthesis based on neighbors preserving principle, characterized in that the method comprising the following steps of:

• (1) obtaining a plurality sets of reference speeches and a user's neutral training speech, and conducting model training to these speeches to obtain a plurality sets of Gaussian Mixture Model (GMM) reference models and the user's neutral training model;
• the reference speeches comprising neutral reference speech and “m” emotion reference models, where m is a natural number greater than 0;
• (2) extracting a neutral-to-emotion Gaussian components transformation set from each set of GMM reference model;
• (3) according to KL (Kullback-Leibler) divergence calculation method, respectively calculating the KL divergence between each neutral training Gaussian component in the neutral training model and neutral reference Gaussian components in all neutral reference models;
• selecting the “n” neutral reference Gaussian components having the smallest KL divergence with each corresponding neutral training Gaussian component;
• then selecting “m” emotion reference Gaussian components corresponding to each neutral reference Gaussian component in “n” neutral reference Gaussian components, where n is a natural number greater than 0;
• (4) combining the selected n×m Gaussian components corresponding to each neutral training Gaussian component to obtain “m” emotion training Gaussian component, and further obtain “m” emotional training models for the user;
• (5) repeating step (1) to step (4) to synthesize the GMM training models for all users;
• (6) inputting a user's test speech, and computing the likelihood score between the test speech and all users' GMM training models, respectively, identifying a corresponding user of the GMM training model with the greatest likelihood score as the speaker to be identified.