디지털융복합연구 (Journal of Digital Convergence)

  • 제13권10호
  • /
  • Pages.353-358
  • /
  • 2015
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

모수 추정을 위한 베이시안 기법과 바타차랴 알고리즘을 융합한 어휘 인식 성능 향상

Vocabulary Recognition Performance Improvement using a convergence of Bayesian Method for Parameter Estimation and Bhattacharyya Algorithm Model

  • 오상엽 (가천대학교 컴퓨터공학과)
  • Oh, Sang-Yeob (Dept. of Computer Engineering, Gachon University)
  • 투고 : 2015.08.09
  • 심사 : 2015.10.20
  • 발행 : 2015.10.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

어휘 인식 시스템은 학습 모델을 구성하여 인식하므로 구성되어진 모델에서 벗어난 어휘의 입력과 유사한 어휘의 입력은 인식하지 못하거나 유사한 어휘로 인식되어 인식률 저하가 나타난다. 이런 경우 인식 모델을 확장할 수 있도록 재구성하거나 인식 모델 구성 시 확장성을 반영하므로 해결할 수 있다. 본 논문에서는 모델 구성 시 확장성을 반영할 수 있는 모수 추정을 위한 베이시안 기법을 사용하여 바타차랴 알고리즘 음성 인식 학습 모델 구성 방법을 융합하여 제안하였다. 음소가 갖는 특징을 기반으로 학습 데이터의 음소에 모수 추정을 위한 베이시안 기법을 이용하였고 유사한 학습 모델은 바타챠랴 알고리즘을 이용하여 정확한 학습 모델로 인식하도록 하였다. 바타챠랴 알고리즘 인식 모델을 구성하여 인식 성능을 평가하였다. 본 논문에서 제안한 시스템을 적용한 결과 어휘 인식률에서 97.5%의 인식률과 1.2초의 학습 시간을 나타내었다.

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. In this case, reconstructing the system in order to add or extend a range of vocabulary is a way to solve the problem. This paper propose configured Bhattacharyya algorithm standing by speech recognition learning model using the Bayesian methods which reflect parameter estimation upon the model configuration scalability. It is recognized corrected standard model based on a characteristic of the phoneme using the Bayesian methods for parameter estimation of the phoneme's data and Bhattacharyya algorithm for a similar model. By Bhattacharyya algorithm to configure recognition model evaluates a recognition performance. The result of applying the proposed method is showed a recognition rate of 97.3% and a learning curve of 1.2 seconds.

키워드

참고문헌

  1. SangYeob Oh. Decision Tree State Tying Modeling Using Parameter Estimation of Bayesian Method. Journal of digital convergence v.13 no.1, pp.243-248, 2015. https://doi.org/10.14400/JDC.2015.13.1.243
  2. SangYeob Oh. Bayesian Method Recognition Rates Improvement using HMM Vocabulary Recognition Model Optimization. Journal of digital convergence v.12 no.7, pp.273-278, 2014. https://doi.org/10.14400/JDC.2014.12.7.273
  3. Jong-Sub Lee, Sang-Yeob Oh. Vocabulary Retrieve System using Improve Levenshtein Distance algorithm. The Journal of digital policy & management v.11 no.11, pp.367-372, 2013.
  4. Sang-Yeob Oh. Decision Tree for Likely phoneme model schema support. The Journal of digital policy & management v.11 no.10, pp.367-372, 2013.
  5. Sang-Yeob Oh. Selective Speech Feature Extraction using Channel Similarity in CHMM Vocabulary Recognition. The Journal of digital policy & management v.11 no.10, pp.453-458, 2013.
  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. Chan-Shik Ahn, Sang-Yeob Oh. CHMM Modeling using LMS Algorithm for Continuous Speech Recognition Improvement. The Journal of digital policy & management v.10 no.11, pp.377 - 382, 2012.
  9. Chan-Shik Ahn, Sang-Yeob Oh. Gaussian Model Optimization using Configuration Thread Control In CHMM Vocabulary Recognition. The Journal of digital policy &management v.10 no.7, pp.167-172, 2012.
  10. Sang-Yeob Oh. Speech Recognition Optimization Learning Model using HMM Feature Extraction In the Bhattacharyya Algorithm. The Journal of digital policy & management v.11 no.6, pp.199-204, 2013.
  11. Beaufays, F., Vanhoucke, V., & Strope, B. Unsupervised discovery and training of maximally dissimilar cluster models. Proc. Interspeech, pp. 66-69, 2010.
  12. Young, S. HTK: Hidden Markov Model Toolkit V3.4.1. Cambridge University, Engineering Department, Speech Group. 1993.
  13. 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. v.15, no.1, pp.177-183, 2010. https://doi.org/10.9708/jksci.2010.15.1.177
  14. 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.
  15. Sang-Yeob Oh. 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, The Journal of digital policy and management. Vol. 13, No. 8 pp. 295-300, 2015