Optimisation of neural models for speaker identification

An approach to speaker recognition based on feedforward neural models is investigated. Each person known to the system has a personalized neural net that is trained to be active for only that person's speech. By including speech from many people in the training data of each net this approach can directly model differences in people's speech. The chosen architecture and amount of training performed is shown to strongly affect the recognition performance. Large models with two hidden layers are shown to be inferior to models with only a single hidden layer and fewer weights. Recognition performance is shown to be comparable to that of a vector quantization approach based on personalized codebooks. The neural approach outperforms the codebook system for small model sizes but does slightly less well for larger models. A multitransputer implementation used in the training phase is described. Near linear speedup is obtained by splitting the training data to given independent subtasks, and a dynamic allocation scheme is used to assign these tasks to processors.>