Noise Removal using a Convergence of the posteriori probability of the Bayesian techniques vocabulary recognition model to solve the problems of the prior probability based on HMM

HMM을 기반으로 한 사전 확률의 문제점을 해결하기 위해 베이시안 기법 어휘 인식 모델에의 사후 확률을 융합한 잡음 제거

  • Oh, Sang-Yeob (Dept. of Computer Media Convergence, Gachon University)
  • 오상엽 (가천대학교 컴퓨터공학과)
  • Received : 2015.06.20
  • Accepted : 2015.08.20
  • Published : 2015.08.28


In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.


  1. Chan-Shik Ahn, Sang-Yeob Oh. Gaussian Model Optimization using Configuration Thread Control In CHMM Vocabulary Recognition. The Journal of Digital Policy and Management. Vol. 10, No. 7, pp. 167-172, 2012.
  2. Chan-Shik Ahn, Sang-Yeob Oh. Echo Noise Robust HMM Learning Model using Average Estimator LMS Algorithm. The Journal of Digital Policy and Management. Vol. 10, No. 10, pp. 277-282, 2012.
  3. Chan-Shik Ahn, Sang-Yeob Oh. Efficient Continuous Vocabulary Clustering Modeling for Tying Model Recognition Performance Improvement. Journal of the Korea Society of Computer and Information. Vol. 15, No. 1, pp. 177-183, 2010.
  4. Chan-Shik Ahn, Sang-Yeob Oh. CHMM Modeling using LMS Algorithm for Continuous Speech Recognition Improvement. The Journal of digital policy and management. Vol. 10, No. 11, pp. 377-382, 2012.
  5. Chan-Shik Ahn, Sang-Yeob Oh. Vocabulary Recognition Retrieval Optimized System using MLHF Model. Journal of the Korea Society of Computer and Information. Vol. 14, No. 10, pp. 217-223, 2009.
  6. A. Srinivasan, Speech Recognition Using Hidden Markov Model, Applied Mathematical Sciences, vol. 5, no. 79, pp. 3943-3948, 2011.
  7. S. M. Naqvi, M. Yu, J. A. Chamber. A Multimodal Approach to Blind Source Separation of Moving Sources. IEEE Trans. Signal Processing. Vol. 4, No. 5, pp. 895-910, 2010.
  8. Beaufays, F., Vanhoucke, V., & Strope, B. Unsupervised discovery and training of maximally dissimilar cluster models. Proc. Interspeech, pp. 66-69, 2010.
  9. Sang-Yeob Oh. Improving Phoneme Recognition based on Gaussian Model using Bhattacharyya Distance Measurement Method. Journal of Korea Multimedia Society. Vol. 14, No. 1, pp. 85-93, 2011.
  10. Hirsch, H. G. & Pearce, D. The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions, in Proc. ICSLP. pp. 18-20. 2000.
  11. Young, S. HTK: Hidden Markov Model Toolkit V3.4.1. Cambridge University, Engineering Department, Speech Group. 1993.
  12. Jong-Young Ahn, Sang-Bum Kim, Su-Hoon Kim, Kang-In Hur. A study on Voice Recognition using Model Adaptation HMM for Mobile Environment. The Journal of the Institute of Webcasting, Internet and Telecommunication. Vol. 11, No. 3, pp. 175-179, 2011.
  13. Sang-Yeob Oh. Selective Speech Feature Extraction using Channel Similarity in CHMM Vocabulary Recognition. The Journal of digital policy and management. Vol. 11, No. 7, pp. 453-458, 2013.
  14. Sang-Yeob Oh. Bayesian Method Improve Recognition Rates using HMM Vocabulary Recognition Model Optimization. The Journal of digital policy and management. Vol. 12, No. 7, pp. 273-278, 2014.
  15. Sang-Yeob Oh. Decision Tree State Tying Modeling Using Parameter Estimation of Bayesian Method The Journal of Digital Policy and Management. Vol. 13, No. 1, pp. 1243-248, 2015.