• Journal of the Korean Society for Railway
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• v.14 no.3
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• pp.228-233
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• 2011

This paper presented the method about the Implementation of Speech Recognition based on phoneme considering the phonological characteristic for Korean Subway Station Names. The Pronunciation dictionary considering PLU set and phonological variations with four Case in order to select the optimum PLU used for Speech Recognition based on phoneme for Korean Subway Station Names was comprised and the recognition rate was estimated. In the case of the applied PLU, we could know the optimum recognition rate(97.74%) be shown in the triphone model in case of considering the recognition unit division of the initial consonant and final consonant and phonological variations.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.285-290
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• 2009

This paper implemented a Speech Recognition System in order to recognize Commands of Ticketing Machine (314 station-names) at real-time using Continuous Hidden Markov Model. Used 39 MFCC at feature vectors and For the improvement of recognition rate composed 895 tied-state triphone models. System performance valuation result of the multi-speaker-dependent recognition rate and the multi-speaker-independent recognition rate is 99.24% and 98.02% respectively. In the noisy environment the recognition rate is 93.91%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.90-97
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• 2003

This paper proposes a speech recognition based on HMM(Hidden Markov Model) using FSVQ(First Section Vector Quantization) and fuzzy concept. In the proposed paper, we generate codebook of First Section, and then obtain multi-observation sequences by order of large propabilistic values based on fuzzy rule from the codebook of the first section. Thereafter, this observation sequences of first section from codebooks is trained and in case of recognition, a word that has the most highest probability of first section is selected as a recognized word by same concept. Train station names are selected as the target recognition vocabulary and LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments of proposed method, we experiment the other methods under same conditions and data. Through the experiment results, it is proved that the proposed method based on HMM using FSVQ and fuzzy concept is superior to tile others in recognition rate.