Speaker independent Digit Recognition System Suma Swamy Research Scholar Anna University, Chennai 10/22/2015 9:10 PM 1

2 Outline Introduction Existing Model Proposed Model Experimental Results Conclusion Outline Introduction Existing Model Proposed Model Experimental Results Conclusion 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM3 Outline Introduction Existing Model Proposed Model Experimental Results Conclusion Digit Recognition system Digits 0 to 9 Feature Extraction: MFCC Template Matching: HMM Noise Reduction: End Point Detection Introduction 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM 4 Outline Introduction Existing Model Proposed Model Experimental Results Conclusion Existing Model Mangesh S. Deshpande, Raghunath S. Holambe: “Text – Independent Speaker Identification using Hidden Markov Models”, 2008 IEEE. CDHMM gives the efficiency of 100% for 400 speakers. 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM 5 Outline Introduction Existing Model Proposed Model Experimental Results Conclusion Proposed Model Digit Recognition System Make Decision & Display Training HMM Models Training HMM Models MFCC Feature Extraction MFCC Feature Extraction Recording Training Utterances Recording Training Utterances Offline/Online Calculate Likelihood Scores 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM 6 Outline Introduction Existing Model Proposed Model Experimental Results Conclusion Proposed Model Digit Recognition System 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM7 Outline Introduction Existing Model Proposed Model Experimental Results Conclusion Proposed Model The probability of occurrence of the observation sequence, P( O ) - Forward algorithm -Backward algorithm -Forward-Backward Procedure Adjust the HMM model parameters to maximize P(O ) or P( O,I ) - The Segmental K-means Algorithm - The Baum-Welch re-estimation 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM8 Experimental Results Outline Existing Model Proposed Model Experimental Results Conclusion 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM9 Outline Existing Model Proposed Model Experimental Results Conclusion Techniques MFCC(Feature Extraction) HMM(Template Matching) End Point Detection(Noise Reduction) Improved efficiency for speaker dependent digit recognition system than speaker independent digit recognition system. This work can be extended from isolated word recognition to continuous speech recognition. 47th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM10 References [1]Tobias Herbig, Franz Gerl, Wolfgang Minker, Simultaneous Speech Recognition and Speaker identification, IEEE, 2010 [2] [3] L R Rabiner And R W Schafer, Digital Processing Of Speech Signals, Pearson Education, [4] Ben Gold and Nelson Morgan, Speech and Audio Signal Processing, John Wiley and Sons, [5] Stephen J. Chapman, MATLAB Programming for Engineers, Thomson Engineering, [6] Lawrence Rabiner, Biing Hwang Juang, B Yegnanarayana, Fundamentals of Speech Recognition, Pearson Education, [7] Martin Wolf, Climent Nadeu, Evaluation of Different Feature Extraction Methods for Speech Recognition in Car Environment, IEEE, [8] Jungpyo Hong, Seungho Han, Sangbae Jeong and Minsoo Hahn, Adaptive Microphone Array Processing for High-Performance Speech Recognition in Car Environment, IEEE, [9] Which Model for Future Speech Recognitione Systems: Hidden Markov Models or Finite-State Automata?, J. Di Martino, J.F. Mari, B. Mathieu, K. Perot, K. Smaili. CRIN- CNRS & INRIA-LORRAIKE., Acoustics, Speech, and Signal Processing, ICASSP-94., IEEE International Conference th Annual National Convention Of Computer Society Of India Organized By : CSI Kolkata Chapter, 1-2 December, 2012 at Science City Kolkata

10/22/2015 9:10 PM11 Thank You