Voice Recognition Performance Improvement using the Convergence of Bayesian method and Selective Speech Feature

베이시안 기법과 선택적 음성특징 추출을 융합한 음성 인식 성능 향상

  • 황재천 (가천대학교 컴퓨터공학과)
  • Received : 2016.11.01
  • Accepted : 2016.12.20
  • Published : 2016.12.31


Voice recognition systems which use a white noise and voice recognition environment are not correct voice recognition with variable voice mixture. Therefore in this paper, we propose a method using the convergence of Bayesian technique and selecting voice for effective voice recognition. we make use of bank frequency response coefficient for selective voice extraction, Using variables observed for the combination of all the possible two observations for this purpose, and has an voice signal noise information to the speech characteristic extraction selectively is obtained by the energy ratio on the output. It provide a noise elimination and recognition rates are improved with combine voice recognition of bayesian methode. The result which we confirmed that the recognition rate of 2.3% is higher than HMM and CHMM methods 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.