Automatic Speaker Identification Using Reusable and Retrainable Binary-Pair Partitioned Neural Networks

This thesis presents an extension of the work previously done on speaker identification using Binary Pair Partitioned (BPP) neural networks. In the previous work, a separate network was used for each pair of speakers in the speaker population. Although the basic BPP approach did perform well and had a simple underlying algorithm, it had the obvious disadvantage of requiring an extremely large number of networks for speaker identification with large speaker populations. It also requires training of networks proportional to the square of the number of speakers under consideration, leading to a very large number of networks to be trained and correspondingly large brining and evaluation times. In the present work, the concepts of clustered speakers and reusable binary networks are investigated. Systematic methods are explored for using a network originally trained to separate only two specific speakers to also separate other speakers of other speaker pairs. For example, it would seem quite likely that a network trained to separate a particular female speaker from a particular male speaker would also reliably separate many other male speakers from many other female speakers. The focal point of the research is to develop a method for reducing the training time and the number of networks required to achieve a desired performance level. A new method of reducing the network requirement is developed along with another method to improve the accuracy to compensate for the expected loss resulting from the network reduction (compared to the BPP approach). The two methods investigated are-reusable binary-paired partitioned neural networks (RBPP) and retrained and reusable binary-pair partitioned neural networks (RRBPP). Both the methods explored and described in this thesis work very well for clean (studio quality) speech-but do not provide the desired level of performance with bandwidth — limited speech (telephone quality). In this thesis, a detailed description of both the methods and the experimental results is provided. All experimental results reported are based on either the Texas Instruments Massachusetts Institute of Technology (TIMIT) or Nynex TIMIT (NTIMIT) databases, using 8 sentences (approximately 24 seconds) for training and up to two sentences (approximately 6 seconds for testing). Best results obtained with TIMIT, using 102 speakers, for BPP, RBPP, and RRBPP respectively (for 2 sentences i.e., — 6 seconds of test data) are 99.02 %, 99.02 %, 99.02 % of speakers correctly identified. Corresponding recognition rates for NTIMIT, again using 102 speakers, are 84.3%, 75.5% and 77.5%. Using all 630 speakers, the accuracy rates for TIMIT are 99%, 97% and 96%, and the accuracy rates for NTIMIT are -72 %, 48% and 41 %.