Isolated Digit Recognition Using in Ear Microphone Data Using MFCC,VQ and HMM

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-12 Number-7
Year of Publication : 2014
Authors : Mahesh K. Patil , Prof. Dr.(Mrs.) L.S. Admuthe , Prashant P. Zirmite


Mahesh K. Patil , Prof. Dr.(Mrs.) L.S. Admuthe , Prashant P. Zirmite. "Isolated Digit Recognition Using in Ear Microphone Data Using MFCC,VQ and HMM", International Journal of Engineering Trends and Technology (IJETT), V12(7),322-325 June 2014. ISSN:2231-5381. published by seventh sense research group


This paper implements the isolated digit recognizer in three steps. The first step performs the endpoint detection and speech segmentation by short term analysis. The second step involves the speech feature extraction by using Mel Frequency Cepstral Coefficients (MFCC) parameters. Finally the third step involves the Vector Quantization (VQ) – Hidden Markov Model (HMM) based classifier for isolated digit recognition. An English spoken digit database which contains number of speakers is used for the testing and validation modules.


[1] Lawrence Rabiner, Biing Hwang Juang, Fundamental of Speech Recognition, Copyright 1999 by AT&T.
[2] GIN-DER WU AND YING LEI “ A Register Array based Low power FFT Processor for speech recognition” Department of Electrical engineering national Chi Nan university Puli545 Taiwan.
[3] B. Yegnanarayana, S.R.M. Prasanna, J. M. Zachariah, and C.S. Gupta, “Combining evidence from source, suprasegmental and spectral features for a fixed- text specker verification system,” IEEE Trans. Speech Audio Process.,vol.13(4), pp. 575-82, July2005.
[4] N.Uma Maheswari, A.P.Kabilan, R.Venkatesh, “A Hybrid model of Neural Network Approach for Speaker independent Word Recognition”, International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010 1793-8201.
[5] Satyanarayana “short segment analysis of speech for enhancement” institute of IIT Madras feb.2009
[6] C.S.Myers and L.R.Rabiner, A Level Building Dynamic Time Warping Algorithm for Connected Word Recognition, IEEE Trans. Acoustics, Speech Signal Proc.,ASSP-29:284- 297,April 1981.
[7] H.Sakoe and S.Chiba, Dynamic programming algorithm optimization for spoken word recognition ,IEEE Trans. Acoustics, Speech, Signal Proc., ASSP-26(1).1978
[8] Santosh K.Gaikwad, Bharti W.Gawali and Pravin Yannawar, “A Review on Speech Recognition Technique”, International Journal of Computer Applications (0975 – 8887) Volume 10– No.3, November 2010
[9] Keh-Yih Su, Speech Recognition using weighted HMM and subspace IEEE Transactions on Audio, Speech and Language.
[10] R.K.Moore, Twenty things we still don t know about speech, Proc.CRIM/ FORWISS Workshop on Progress and Prospects of speech Research an Technology, 1994.
[11] Shigeru Katagiri, A New hybrid algorithm for speech recognition based on HMM segmentation and learning Vector quantization, IEEE Transactions on Audio Speech and Language processing Vol.1, No.4
[12] L.R.Rabiner and B.H.jaung,” Fundamentals of Speech Recognition Prentice-Hall, Englewood Cliff, New Jersy, 1993

Speaker recognition, MFCC, Vector Quantization, Hidden Markov Model.