|An Intelligent_Text Independent Speaker Identification using VQ-GMM model based Multiple Classifier System
Abstract: Speaker identification is the task of establishing identity of an individual based on his/her voice characteristics. Speech signal is basically meant to carry the information about the linguistic message. But, it also contains the speaker specific information and thus can be used to recognize (identify/verify) a person.
The Speaker identification task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker-specific feature parameters from the speech. The features are used to generate speaker models. In the testing phase, speech samples from unknown speakers are compared with the models and classified.
Current state of the art speaker recognition systems use the Gaussian mixture model (GMM) technique in combination with the Expectation Maximization (EM) algorithm to build the speaker models. The most frequently used features are the Mel Frequency Cepstral Coefficients (MFCCs), they are extracted to primarily characterize the spectral envelope of a quasi-stationary speech segment.
|Ittansa Yonas Kelbesa
||Department of Information Engineering, Università degli studi di Brescia, Italy