- Volume 12 Issue 7
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
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.
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- 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.
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- Vocabulary optimization process using similar phoneme recognition and feature extraction vol.19, pp.3, 2016, https://doi.org/10.1007/s10586-016-0619-0