Speaker recognition using neural network and adaptive wavelet transform
Mohammad Bodruzzaman, Xingkang Li, Kah E. Kuah, Lamar Crowder, Mohan Malkani, Harold H Szu, Brian A. Telfer · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
The same word uttered by different people has different waveforms. It has also been observed that the same word uttered by the same person has different waveform at different times. This difference can be characterized by some time domain dilation effects in the waveform. In our experiment a set of words was selected and each word was uttered eight times by five different speakers. The objective of this work is to extract a wavelet basis function for the speech data generated by each individual speaker. The wavelet filter coefficients are then used as a feature set and fed into a neural network-based speaker recognition system. This is an attempt to cascade a wavelet network (wavenet) and a neural network (neural-net) for feature extraction and classification respectively and applied for speaker recognition. The results show very high promise and good prospects to couple a wavelet network and neural networks.