Biologically inspired speaker verification

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented. The first is a vowel-dependent speaker verification method that uses a modified Self Organising Map (SOM) algorithm. For each speaker, a seeded SOM is trained to produce representative Discrete Fourier Transform (DFT) models of three vowels from a spoken input using positive samples only. This SOM training is performed both during a registration phase and during each subsequent verification attempt. Speaker verification is achieved by computing the Euclidean distance between the registration and verification SOM trained weight sets. An analysis of the comparative system performance when using DFT input vectors, as well as Linear Prediction Code (LPC) spectrum and Mel Frequency Cepstrum Coefficients (MFCC) alternative input features indicates that the DFT spectrum outperforms both MFCC and LPC features. The algorithm was evaluated using 50 speakers from the Centre for Spoken Language Understanding (CSLU2002) speaker verification database. The second method consists of two neural network stages. The first stage is the modified SOM which now operates as a vowel clustering stage that filters the input speech data and separates it into three sets of vowel information. The second stage then contains three Multi Layer Perceptron (MLP) networks; each acting as a distinct vowel verifier. Adding this second stage allows the use of negative sample training. The input of each MLP network is the respective filtered output vowel data from the first stage. The DFT spectrum is again used as the input feature vector due to its optimal performance in the first algorithm. The overall system was evaluated