Wavelet entropy and neural network for text-independent speaker identification

Authors:
Khaled Daqrouq
Affiliations:
Department of Communication and Electronics, Philadelphia University, Jordan
Venue:
Engineering Applications of Artificial Intelligence
Year:
2011

Citing 11
Cited 2

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Speech Communication
A speaker identification system using a model of artificial neural networks for an elevator application

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Phoneme recognition using wavelet based features

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Rapid and brief communication: Combining classifier decisions for robust speaker identification

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Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders

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Efficient text independent speaker recognition with wavelet feature selection based multilayered neural network using supervised learning algorithm

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An expert system for speaker identification using adaptive wavelet sure entropy

Expert Systems with Applications: An International Journal
Comb and multiplexed wavelet transforms and their applications tosignal processing

IEEE Transactions on Signal Processing
Pitch-synchronous wavelet representations of speech and musicsignals

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Average framing linear prediction coding with wavelet transform for text-independent speaker identification system

Computers and Electrical Engineering
Arabic vowels recognition based on wavelet average framing linear prediction coding and neural network

Speech Communication

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Abstract

In the present study, the techniques of wavelet transform (WT) and neural network were developed for speech based text-independent speaker identification. The first five formants in conjunction with the Shannon entropy of wavelet packet (WP) upon level four features extraction method was developed. Thirty-five features were fed to feed-forward backpropagation neural networks (FFPBNN) for classification. The functions of features extraction and classification are performed using the wavelet packet and formants neural networks (WPFNN) expert system. The declared results show that the proposed method can make an effectual analysis with average identification rates reaching 91.09. Two published methods were investigated for comparison. The best recognition rate selection obtained was for WPFNN. Discrete wavelet transform (DWT) was studied to improve the system robustness against the noise of -2dB.