• 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%.

• The Journal of the Acoustical Society of Korea
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• v.8 no.4
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• pp.60-64
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• 1989

In this paper, an local path constraint Is proposed in order to increase the speech recognition rate. An input speech signal is analyzed by autocorrelation and LPC coefficient as parameters. The local path constraint of the proposed type was compared with the conventional five types. The speechs used in this search are the subway stops, and the 130 words pronounced 10 times for the different 13 words consisting of 11 characters of syllable by 2 male and 1 female are tested. As a result, we proved that this proposed type is the most optimal type and the recognition rate of $94.6\%$ is obtained .

• The Journal of the Acoustical Society of Korea
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• v.31 no.3
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• pp.170-178
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• 2012

In the paper, phonetic decision tree of the triphone unit was built for the phoneme-based speech recognition of 640 stations which run by the Korail. The clustering rate was determined by Pearson and Regression analysis to decide threshold used in node splitting. Using the determined the clustering rate, thresholds are automatically decided by the threshold value according to the average clustering rate. In the recognition experiments for verifying the proposed method, the performance improved 1.4~2.3 % absolutely than that of the baseline system.

• 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.