Review of neural networks for speech recognition
Neural Computation
Ten lectures on wavelets
Fundamentals of speech recognition
Fundamentals of speech recognition
Wavelets and subband coding
IEEE Computational Science & Engineering
Speech compression with cosine and wavelet packet near-best bases
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
A new approach in wavelet based speech compression
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
A comprehensive approach for speech related multimedia applications
WSEAS Transactions on Signal Processing
Radial basis functions with wavelet packets for recognizing Arabic speech
CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control & signal processing
On the distribution of zeros for daubechies orthogonal wavelets and associated polynomials
MATH'10 Proceedings of the 15th WSEAS international conference on Applied mathematics
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In this paper, a Neural Network (NN) approach for the recognition of the Arabic digits is presented. The two phases of training and testing in a Radial Basis Functions (RBF) type network is described. Biorthogonal Wavelets are constructed and used for analysis of generated subwords of the digits. This approach decomposes spoken Arabic digits based on the acoustical information contained within the speech signals. The procedure locates the boundaries between subwords by finding the peaks in the function representing the spectral changes between consecutive speech frames. The Frame-based energy parameters derived from a Wavelet Packet Scale (WPS) are used in deriving the Spectral Variation Function (SVF). Three Biorthogonal wavelets are used as analyzing functions and their performances are compared with that of their Orthogonal counterpart and with that of the traditional Fourier based Mel scale approach.