Implementation of a Novel Phoneme Recognition System Using TMS320C6713 DSP

A number of techniques have been proposed in the literature for phoneme based speech recognition system. In this paper, a technique for automatic phoneme recognition using zero-crossings (ZC) and magnitude sum function (MSF) is proposed. The number of zero-crossings and Magnitude sum function per frame are extracted and a Minimum Distance Classifier is proposed to recognize the phonemes in each frame with these features. In order to increase the recognition accuracy of phonemes, a finite state machine is also proposed. The performance of the proposed phoneme recognition system is evaluated using TTS database and compared with the system using Linear Predictive Coefficients (LPC) feature inputs. Phoneme recognition accuracies of 70.93% and 55.25% are obtained for the system using LPC and the one using ZC along with MSF respectively. However, using the finite state machine proposed in this paper, 100% recognition accuracy is obtained for both the techniques. The computational costs required for recognizing various sentences using both of the feature extraction techniques are evaluated. It is observed that the proposed technique requires about 9.3 times lower computational cost than the one using LPC. The proposed technique is adopted for the implementation of the phoneme recognition system on Texas Instruments TMS320C6713 floating point processor. The different ways to reduce the recognition time for the target device is explored and reported in this paper. The technique proposed here is also applicable for speech inputs from other database.

[1]  Patrick Wambacq,et al.  Automatic Phonemic Labeling and Segmentation of Spoken Dutch , 2004, LREC.

[2]  Youngjik Lee,et al.  Phoneme segmentation of continuous speech using multi-layer perceptron , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[3]  Isabel Trancoso,et al.  Large Vocabulary Continuous Speech Recognition Using Weighted Finite-State Transducers , 2002, PorTAL.

[4]  B. Venkataramani,et al.  FPGA implementation of isolated digit recognition system using modified back propagation algorithm , 2008, 2008 International Conference on Electronic Design.

[5]  Wai C. Chu,et al.  Speech Coding Algorithms , 2003 .

[6]  E. Chilton,et al.  Time-delay radial basis functions in silicon for phoneme recognition , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[7]  B. Venkataramani,et al.  FPGA Implementation of Support Vector Machine Based Isolated Digit Recognition System , 2009, 2009 22nd International Conference on VLSI Design.

[8]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[9]  B. Venkataramani,et al.  SOC Implementation of HMM Based Speaker Independent Isolated Digit Recognition System , 2007, 20th International Conference on VLSI Design held jointly with 6th International Conference on Embedded Systems (VLSID'07).

[10]  Rulph Chassaing,et al.  Digital Signal Processing and Applications with the C6713 and C6416 DSK , 2004 .

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

[12]  Mehryar Mohri,et al.  Finite-State Transducers in Language and Speech Processing , 1997, CL.

[13]  Suresh Manandhar,et al.  Phoneme segmentation of speech , 2006, 18th International Conference on Pattern Recognition (ICPR'06).