The synergism of web and phone technologies has led to a new innovative voice web network.The voice web requires a voice recognition and authentication system incorporating a reliable speech recognition technique for secure information access across the Internet. In line with this requirement, we investigate the applicability of artijicial neural networks to speech recognition. In our experiment, a total number of 200 vowel signals from individuals with dijferent gender and races were recorded. The )filtering process was performed using the wavelet approach to de-noise and to compress the speech signals. An artificial neural network, specially the Probabilistic Neural Network model, was then employed to recognize and to classijjj vowel signals into the respective categories. A series of parameter settings for the PNN model was investigated, and the results obtained were analyzed and discussed.
An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.
This paper addresses the problem of speaker recognition from speech signals. The study focuses on the development of a speaker recognition system comprising two modules: a wavelet-based feature extractor, and a neural-network-based classijier. We have conducted a number of experiments to investigate the applicability of Discrete Wavelet Transform (D WT) in extracting discriminative features from the speech signals, and have examined various models from the Adaptive Resonance Theory (ART) family of neural networks in classijjing the extracted features. The results indicate that DWT could be a potential feature extraction tool f o r speaker recognition.In addition, the ART-based classijiers have yielded very promising recognition accuracy at more than 81%.
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