Speaker recognition using dynamic synapse-neural networks

We have developed a speaker identification system that performs well under two situations that current identification system encounter difficulties: when the speaker's voice is combined with interfering noise, and when multiple speakers have similar voices. Our system consists of several serial processors. First, 4 Chebyshev type 1 filters separate the sound signal into 4 overlapping spectral ranges. Each of the 4 signals is passed through a wavelet filter that utilizes a 3 level, Daubechies 4th order discrete wavelet transformation, generating 1 approximation and 3 detail coefficient arrays. The resulting 16 signals are input for two gender-specific Dynamic Synapse Neural Networks that have 3 outputs corresponding to each of the 3 male or 3 female speakers in our study. The energy in each of the pulse train outputs of the DSNN is compared, the output with the highest energy is identified, and the corresponding speaker is chosen. The choice of which DSNN to use is determined by a gender identifier that has been developed in our laboratory. The DSNNs were trained using a Genetic Algorithm training method.