Design of a hardware-based discrete wavelet transform architecture for phoneme recognition

This paper presents the design of a digital hardware implementation based on Discrete Wavelet Transforms (DWTs) for the task of feature extraction in a multi-speaker phoneme recognition system. This is the first research where the design of a hardware-based DWT design is directed towards a speech recognition application. In the proposed architecture, the lifting-scheme approach employing the orthogonal Daubechies wavelet of order 5 was considered. The designed system was synthesised on a Xilinx Virtex-II XC2V3000 FPGA, and evaluated with the TIMIT corpus. This hardware-based DWT architecture is then intended to be implemented on a dedicated chip, along with the hardware implementation of the classification stage of the proposed phoneme recognition system, in order to further improve the resultant performance.

[1]  O. Casha,et al.  Neural network architectures for speaker independent phoneme recognition , 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

[2]  Ivan Grech,et al.  Comparative study of automatic speech recognition techniques , 2013, IET Signal Process..

[3]  Ivan Grech,et al.  Discrete wavelet transforms with multiclass SVM for phoneme recognition , 2013, Eurocon 2013.

[4]  Bruce F. Cockburn,et al.  Efficient architectures for 1-D and 2-D lifting-based wavelet transforms , 2004, IEEE Transactions on Signal Processing.

[5]  Mountassar Maamoun,et al.  VLSI Design of 2-D Discrete Wavelet Transform for Area-Efficient and High-Speed Image Computing , 2008 .

[6]  Chaitali Chakrabarti,et al.  A Survey on Lifting-based Discrete Wavelet Transform Architectures , 2006, J. VLSI Signal Process..

[7]  S. Chauhan,et al.  Speech Compression FPGA Design by Using Different Discrete Wavelet Transform Schemes , 2008, Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science 2008.

[8]  Ivan Grech,et al.  Comparison of different multiclass SVM methods for speaker independent phoneme recognition , 2012, 2012 5th International Symposium on Communications, Control and Signal Processing.

[9]  Pi-Lian He,et al.  The Clustering Solution of Speech Recognition Models with SOM , 2006, ISNN.

[10]  Sayed Ahmad Salehi,et al.  VLSI Architectures of Lifting-Based Discrete Wavelet Transform , 2011 .

[11]  Ivan Grech,et al.  Support Vector Machines with the priorities method for speaker independent phoneme recognition , 2011, 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[12]  Ivan Grech,et al.  Hardware-based support vector machine for phoneme classification , 2013, Eurocon 2013.

[13]  Jan Nouza,et al.  System for automatic collection, annotation and indexing of Czech broadcast speech with full-text search , 2010, Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference.