Speaker recognition using dynamic synapse based neural networks with wavelet processing
Sageev George, Theodore W. Berger, Alireza Afshordi Dibazar, Walter M. Yamada · The Journal of the Acoustical Society of America · 2003
We have designed systems utilizing a dynamic synapse neural network (DSNN) to perform speech recognition tasks. The DSNN architecture has demonstrated noise-resistive properties in previous research. The speech signal is first passed through a bank of bandpass filters. The output of each filter then undergoes wavelet transformation to extract speaker-dependent features. Transformed signals are used as input for a DSNN, which is trained using a genetic algorithm training method. We are seeking to explore the capabilities of a biologically based neural network in solving a problem which humans are still better at solving than conventional solutions. The dynamic synapse neural networks were trained to perform several speech recognition tasks, including speaker verification, speaker identification, and word recognition. In the speaker verification task, the objective was to distinguish one speaker from a group of up to 22 imposters based on the utterance of a codeword. In the speaker identification task, the objective was to identify which of 6 speakers has uttered the codeword. In the word recognition task, the objective was to perform speaker-dependent word recognition on a set of 30 words. Neural networks were capable of 90% correct identifications in the presence of white noise (0 dB SNR).