Improved tree model for arabic speech recognition

This paper introduces a fast learning method for a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition by means of a new proposed spanning tree structure that takes advantage of the temporal nature of speech signal. The experimental results obtained on a spoken Arabic digit dataset confirmed that for the same rate of recognition the proposed method, in terms of time computation is much faster than the state of art algorithm that use the maximum weight spanning tree (MWST).

[1]  William M. Campbell,et al.  Generalized linear discriminant sequence kernels for speaker recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Richard P. Lippmann,et al.  Review of Neural Networks for Speech Recognition , 1989, Neural Computation.

[3]  Hervé Bourlard,et al.  Connectionist Speech Recognition: A Hybrid Approach , 1993 .

[4]  Marwan Al-Zabibi.  An acoustic-phonetic approach in automatic arabic speech recognition , 1990 .

[5]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[6]  Steven F. Quigley,et al.  Implementing a simple continuous speech recognition system on an FPGA , 2002, Proceedings. 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines.

[7]  Fabian Vargas,et al.  A FPGA-based Viterbi algorithm implementation for speech recognition systems , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[8]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[9]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[10]  Steve Renals,et al.  Speaker verification using sequence discriminant support vector machines , 2005, IEEE Transactions on Speech and Audio Processing.

[11]  Patrick Kenny,et al.  Eigenvoice modeling with sparse training data , 2005, IEEE Transactions on Speech and Audio Processing.

[12]  Sanaa El Fkihi,et al.  Probability Approximation Using Best-Tree Distribution for Skin Detection , 2006, ACIVS.

[13]  David A. Forsyth,et al.  Mixtures of trees for object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Magne Hallstein Johnsen,et al.  A VLSI implementation of PDF computations in HMM based speech recognition , 1996, Proceedings of Digital Processing Applications (TENCON '96).

[15]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[16]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[17]  Nacereddine Hammami,et al.  Tree distribution classifier for automatic spoken Arabic digit recognition , 2009, 2009 International Conference for Internet Technology and Secured Transactions, (ICITST).

[18]  Driss Aboutajdine,et al.  The mixture of K-Optimal-Spanning-Trees based probability approximation: Application to skin detection , 2008, Image Vis. Comput..

[19]  Oliver Chiu-sing Choy,et al.  An HMM-based speech recognition IC , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..