• Proceedings of the Acoustical Society of Korea Conference
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• 한국음향학회 1993년도 학술논문발표회 논문집 제12권 1호
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• pp.121-124
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• 1993

This paper is a study on the word recognition using neural network. A limited vocabulary, speaker independent, isolated word recognition system has been built. This system recognizes isolated word without performing segmentation, phoneme identification, or dynamic time wrapping. It needs a static pattern approach to recognize a spatio-temporal pattern. The preprocessing only includes preceding and tailing silence removal, and word length determination. A LPC analysis is performed on each of 24 equally spaced frames. The PARCOR coefficients plus 3 other features from each frame is extracted. In order to simplify a structure of neural network, we composed binary code form to decrease output nodes.

• Journal of the Institute of Convergence Signal Processing
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• 제2권3호
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• pp.16-23
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• 2001

In this paper, we propose the pseudo n-gram language models for speech recognition with middle size vocabulary compared to large vocabulary speech recognition using the statistical n-gram language models. The proposed method is that it is very simple method, which has the standard structure of ARPA and set the word probability arbitrary. The first, the 1-gram sets the word occurrence probability 1 (log likelihood is 0.0). The second, the 2-gram also sets the word occurrence probability 1, which can only connect the word start symbol and WORD, WORD and the word end symbol . Finally, the 3-gram also sets the ward occurrence probability 1, which can only connect the word start symbol , WORD and the word end symbol . To verify the effectiveness of the proposed method, the word recognition experiments are carried out. The preliminary experimental results (off-line) show that the word accuracy has average 97.7% for 452 words uttered by 3 male speakers. The on-line word recognition results show that the word accuracy has average 92.5% for 20 words uttered by 20 male speakers about stock name of 1,500 words. Through experiments, we have verified the effectiveness of the pseudo n-gram language modes for speech recognition.

• Proceedings of the IEEK Conference
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.1019-1022
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• 1999

In this paper, we analysis the effect of a dictionary in a handwritten Hangul word recognition problem in terms of its size and the length of the words in it. With our experimental results, we can account for the word recognition rate depending not only on character recognition performance, but also much on the amount of the information that the dictionary contains, as well as the reduction rate of a dictionary.

• Journal of the Korean Institute of Telematics and Electronics
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• 제25권12호
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• pp.1541-1547
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• 1988

In this paper, a new model for isolated word recognition called segment probability model is proposed. The proposed model is composed of two procedures of segmentation and modelling each segment. Therefore the spoken word is devided into arbitrary segments and observation probability in each segments is obtained using vector quantization. The proposed model is compared with pattern matching method and hidden Markov model by recognition experiment. The experimental results show that the proposed model is better than exsisting methods in terms of recognition rate and caculation amounts.

• The Transactions of the Korean Institute of Electrical Engineers
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• 제40권4호
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• pp.351-359
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• 1991

This paper describes performances of speaker adaptation using a probabilistic spectral mapping matrix in hidden-Markov model(HMM) -based Korean isolated word recognition. Speaker adaptation based on probabilistic spectral mapping uses a well-trained prototype HMM's and is carried out by Viterbi, dynamic time warping, and forward-backward algorithms. Among these algorithms, the best performance is obtained by using the Viterbi approach together with codebook adaptation whose improvement for isolated word recognition accuracy is 42.6-68.8 %. Also, the selection of the initial values of the matrix and the normalization in computing the matrix affects the recognition accuracy.

• Proceedings of the Acoustical Society of Korea Conference
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• 한국음향학회 1995년도 제12회 음성통신 및 신호처리 워크샵 논문집 (SCAS 12권 1호)
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• pp.69-72
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• 1995

In this paper, a new approach for Korean connected digits recognition using the spatio-temporal neural network is reported. The data of seven digits phone numbers are used in the recognition of connected words, and in the initial experiment, digit recognition rate of 28% was achieved. In this paper, to increase recognition rate, two different approaches are analyzed. In the first system, to compensate the STNN's own defect and to emphasize the Korean word's phonic characters, the starting point of phone is pointed by comparing the average magnitude and zero-crossing rate and the ending point is pointed by comparing only zero-crossing rate. The digit recoginiton rate increased to 61%. Also, in the second system, to consider fact that same word's phone is varied severally, the number of STNN's of each word is increased from one to five, and then the varied same word's phones can be included to the increased STNN's. The digit recogniton rate of connected words increased to 89%.

• The Journal of the Acoustical Society of Korea
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• 제13권5호
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• pp.24-31
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• 1994

In this paper, we study continuous speech recognition of Korean language using acoustic models of word juncture coarticulation. To alleviate the performance degradation due to coarticulation problems, we use context-dependent units that model inter-word transitions in addition to intra-word transitions. In all cases the initial phone of each word has to be specified for each possible final phone of the previous word similarly for the final phone of each word. To improve the robustness of the HMM parameters, the covariance matrix is smoothed. We also use position-dependent units to improve the discriminative power between units. Simulation results show that when the improved models of word juncture coarticulation are used. the recognition performance is considerably improved compared to the baseline system using only intra-word units.

• Speech Sciences
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• 제8권3호
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• pp.123-132
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• 2001

In recent years research on HCI technology has been very active and speech recognition is being used as its typical method. Its recognition, however, is deteriorated with the increase of surrounding noise. To solve this problem, studies concerning the multimodal HCI are being briskly made. This paper describes automated lipreading for bimodal speech recognition on the basis of image- and speech information. It employs audio-visual DB containing 1,074 words from 70 voice and tri-viseme as a recognition unit, and state tied HMM as a recognition model. Performance of automated recognition of 22 to 1,000 words are evaluated to achieve word recognition of 60.5% in terms of 22word recognizer.

• Korean Journal of Cognitive Science
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• 제20권3호
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• pp.243-274
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• 2009

The association, word frequency and imagery have been considered as the main factors that affect the word recognition. The present study aimed to examine the imagery effect and the interaction of the association effect while controlling the frequency effect. To explain the imagery effect, we compared the two theories (dual-coding theory, context availability model). The lexical decision task using priming paradigm was administered. The duration of prime words was manipulated as 20ms, 50ms, and 450ms in experiments 1, 2, and 3, respectively. The association and imagery of prime words were manipulated as the main factors in each of the three experiments. In experiment 1, the duration of prime words (20ms) which is expected to not activate the semantic context enough to affects the word recognition was used. As a result, only imagery effect was statically significant. In experiment 2, the duration of prime word was 50ms, which we expected to activate the semantic context without perceptual awareness. The result showed both the association and imagery effects. The interaction between the two effects was also significant. In experiment 3, to activate the semantic context with perceptual awareness, the prime words were presented for 450ms. Only association effect was statically significant in this experimental condition. The results of the three experiments suggest that the influence of the imagery was at the early stages of word recognition, while the association effect appeared rather later than the imagery. These results implied that the two theories are not contrary to each other. The dual-coding theory just concerned imagery effect which affects the early stage of word recognition, and context-availability model is more for the semantic context effect which affects rather later stage of word recognition. To explain the word recognition process more completely, some integrated model need to be developed considering not only the main 3 effects but also the stages which extends along the time course of the process.

• International Journal of Ocean System Engineering
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• 제2권1호
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• pp.24-31
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• 2012

This paper introduce a technique for Word segmentation for the handwritten recognition of Urdu script. Word segmentation or word tokenization is a primary technique for understanding the sentences written in Urdu language. Several techniques are available for word segmentation in other languages but not much work has been done for word segmentation of Urdu Optical Character Recognition (OCR) System. A method is proposed for word segmentation in this paper. It finds the boundaries of words in a sequence of ligatures using probabilistic formulas, by utilizing the knowledge of collocation of ligatures and words in the corpus. The word identification rate using this technique is 97.10% with 66.63% unknown words identification rate.