DOI QR코드

DOI QR Code

Bayesian Method Recognition Rates Improvement using HMM Vocabulary Recognition Model Optimization

HMM 어휘 인식 모델 최적화를 이용한 베이시안 기법 인식률 향상

  • Oh, Sang Yeon (Dept. of Computer Media Convergence, Gachon University)
  • 오상엽 (가천대학교 컴퓨터미디어융합학과)
  • Received : 2014.05.10
  • Accepted : 2014.07.20
  • Published : 2014.07.28

Abstract

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

Keywords

HMM;Vocabulary Recognition;Model Optimize;Bayesian;Recognition Rate

References

  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. https://doi.org/10.9708/jksci.2010.15.1.177
  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. Y. Shao, S. Srinivasan, Z. Jin, D. Wang. A Computational Auditory Scene Analysis System for Robust Speech Recognition. Computer Speech & Language. Vol. 24, No. 1, pp. 77-93, 2010. https://doi.org/10.1016/j.csl.2008.03.004
  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. S. Y. Cho, D. M. Sun, Z. D. Qiu. A Spearman correlation coefficient ranking for matching-score fusion on speaker recognition. Proc. TENCON Conf. pp. 736-741, 2011.
  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. https://doi.org/10.9717/kmms.2011.14.1.085
  10. 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.
  11. Sang-Yeob Oh. Selective Speech Feature Extraction using Channel Similarity in CHMM Vocabulary Recognition. The Journal of digital policy and management. Vol. 11, No. 10, pp. 453-458, 2013. https://doi.org/10.14400/JDPM.2013.11.10.453

Cited by

  1. Vocabulary optimization process using similar phoneme recognition and feature extraction vol.19, pp.3, 2016, https://doi.org/10.1007/s10586-016-0619-0