• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.60-65
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• 2002

In this paper, we propose GFCC(gammatone filter frequency cepstrum coefficient) parameter which was based on the auditory characteristic for accomplishing better speech recognition rate. And it is performed the experiment of speech recognition for isolated word acquired from telephone network. For the purpose of comparing GFCC parameter with other parameter, the experiment of speech recognition are carried out using MFCC and LPCC parameter. Also, for each parameter, we are implemented CMS(cepstral mean subtraction)which was applied or not in order to compensate channel distortion in telephone network. Accordingly, we found that the recognition rate using GFCC parameter is better than other parameter in the experimental result.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.4
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• pp.57-65
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• 1997

The purpose of this paper is to propose the HMM (hidden markov model) based on multi-observation sequence for the isolated word recognition. The proosed model generates the codebook of MSVQ by dividing each word into several sections followed by dividing training data into several sections. Then, we are to obtain the sequential value of multi-observation per each section by weighting the vectors of distance form lower values to higher ones. Thereafter, this the sequential with high probability value while in recognition. 146 DDD area names are selected as the vocabularies for the target recognition, and 10LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments by way of the proposed model, for the comparison with it, the experiments by DP, MSVQ, and genral HMM are made with the same data under the same condition. The experiment results have shown that HMM based on multi-observation sequence proposed in this paper is proved superior to any other methods such as the ones using DP, MSVQ and general HMM models in recognition rate and time.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.51-58
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• 1993

This paper describes the development of a speaker-dependent isolated word recognizer as applied to voice dialing in a car noise environment. for this purpose, several methods to improve performance under such condition are evaluated using database collected in a small car moving at 100km/h The main features of the recognizer are as follow: The endpoint detection error can be reduced by using the magnitude of the signal which is inverse filtered by the AR model of the background noise, and it can be compensated by using variants of the DTW algorithm. To remove the noise, an autocorrelation subtraction method is used with the constraint that residual energy obtainable by linear predictive analysis should be positive. By using the noise rubust distance measure, distortion of the feature vector is minimized. The speech recognizer is implemented using the Motorola DSP56001(24-bit general purpose digital signal processor). The recognition database is composed of 50 Korean names spoken by 3 male speakers. The recognition error rate of the system is reduced to 4.3% using a single reference pattern for each word and 1.5% using 2 reference patterns for each word.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1705-1709
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• 2005

This paper covers review and challenges in the area of speech recognition by taking into account different classes of recognition mode. The recognition mode can be either speaker independent or speaker dependant. Size of the vocabulary and the input mode are two crucial factors for a speech recognizer. The input mode refers to continuous or isolated speech recognition system and the vocabulary size can be small less than hundred words or large less than few thousands words. This varies according to system design and objectives.[2]. The organization of the paper is: first it covers various fundamental methods of speech recognition, then it takes into account various deficiencies in the existing systems and finally it discloses the various probable application areas.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.21-30
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• 1987

Length normalization by variable frame size is proposed as a novel approach to length normalization to solve the problem that the length variation of spoken word results in a lowing of recognition accuracy. This method has the advantage of curtailment of recognition time in the recognition stage because it can reduce the number of frames constructing a word compared with length normalization by a fixed frame size. In this paper, variable frame length normalization is applied to multisection vector quantization and the efficiency of this method is estimated in the view of recognition time and accuracy through practical recognition experiments.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.43-49
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• 1996

This research proposes a system for speaker independent Korean continuous speech recognition with 247 DDD area names using keyword spotting technique. The applied recognition algorithm is the Dynamic Programming Neural Network(DPNN) based on the integration of DP and multi-layer perceptron as model that solves time axis distortion and spectral pattern variation in the speech. To improve performance, we classify word model into keyword model and non-keyword model. We make an experiment on postprocessing procedure for the evaluation of system performance. Experiment results are as follows. The recognition rate of the isolated word is 93.45% in speaker dependent case. The recognition rate of the isolated word is 84.05% in speaker independent case. The recognition rate of simple dialogic sentence in keyword spotting experiment is 77.34% as speaker dependent, and 70.63% as speaker independent.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1468-1475
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• 1990

In this paper, isolated words are recognized using multisection VQ and HMM. As recognition vocabuaries, 20 area-name which is uttered 5 times by 3 speakers is selected. In generating codebook, we devide recognition vocabulary into equal length, section, and make standard VQ codebook to each section and calculate observation by section and than recognize isolated words by HMM training. Multisection VQ codebook has time information and as observation is calculated by eacy section, computation is lesser and recongnition rate is higher than by whole codword. As a result, it is proved that recognition rate is higher in case of HMM using multisection VQ codebook.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.905-913
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• 1987

This paper presents a speaker dependent isolated digit recognition algorithm using the Mellin transform. Since the Mellin transform converts a scale information into a phase information, attempts have been made to utilize this scale invariance property of the Mellin transform in order to alleviate a time-normalization procedure required for a speech recognition. It has been found that good results can be obtained by taking the Mellin transform to the features such as a ZCR, log energy, normalized autocorrelation coefficients, first predictor coefficient and normalized prediction error. We employed a difference function for evaluating a similarity between two patterns. When the proposed algorithm was tested on Korean digit words, a recognition rate of 83.3% was obtained. The recognition accuracy is not compatible with the other technique such as LPC distance however, it is believed that the Mellin transform can effectively perform the time-normalization processing for the speech recognition.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.3-8
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• 1999

In speech recognition for real-world applications, the performance degradation due to the mismatch introduced between training and testing environments should be overcome. In this paper, to reduce this mismatch, we provide a hybrid method of spectral subtraction and residual noise masking. We also employ multiple model approach to obtain improved robustness over various noise environments. In this approach, multiple model sets are made according to several noise masking levels and then a model set appropriate for the estimated noise level is selected automatically in recognition phase. According to speaker independent isolated word recognition experiments in car noise environments, the proposed method using model sets with only two masking levels reduced average word error rate by 60% in comparison with spectral subtraction method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.2
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• pp.196-205
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• 1991

This paper is a study on the isolated word recognition of speaker independent which proposes to newly improved MSVQ(multisection vector quantization) recognition system which improve the classical MSVQ recognition system. It is a difference that test pattern has on more section than reference pattern in recognition system 146 DDD area names are selected as recognition vocabulary. 12th LPC cepstral coefficients is used as feature parameter. and when codebook is generated, MINSUM and MINMAX are used in finding the centroid. According to the experiment result. it is proved that this method is better than VQ(vector quantization) recognition methods, DTW(dynamic time warping) pattern matching methods and classical MSVQ methods for recognition rate and recognition time.