• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.83-90
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• 2010

In vocabulary recognition system has reduce recognition rate unrecognized error cause of similar phoneme recognition and due to provided inaccurate vocabulary. Input of inaccurate vocabulary by feature extraction case of recognition by appear result of unrecognized or similar phoneme recognized. Also can't feature extraction properly when phoneme recognition is similar phoneme recognition. In this paper propose vocabulary recognition post-process error correction system using phoneme likelihood based on phoneme feature. Phoneme likelihood is monophone training phoneme data by find out using MFCC and LPC feature extraction method. Similar phoneme is induced able to recognition of accurate phoneme due to inaccurate vocabulary provided unrecognized reduced error rate. Find out error correction using phoneme likelihood and confidence when vocabulary recognition perform error correction for error proved vocabulary. System performance comparison as a result of recognition improve represent MFCC 7.5%, LPC 5.3% by system using error pattern and system using semantic.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2246-2253
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• 2000

In this paper, we show that one can enhance the performance of blind segmentation of phoneme boundaries by adopting the knowledge of Korean syllabic structure and the regions of voiced/unvoiced sounds. eh proposed method consists of three processes : the process to extract candidate phoneme boundaries, the process to detect boundaries of voiced/unvoiced sounds, and the process to select final phoneme boundaries. The candidate phoneme boudaries are extracted by clustering method based on similarity between two adjacent clusters. The employed similarity measure in this a process is the ratio of the probability density of adjacent clusters. To detect he boundaries of voiced/unvoiced sounds, we first compute the power density spectrum of speech signal in 0∼400 Hz frequency band. Then the points where this paper density spectrum variation is greater than the threshold are chosen as the boundaries of voiced/unvoiced sounds. The final phoneme boundaries consist of all the candidate phoneme boundaries in voiced region and limited number of candidate phoneme boundaries in unvoiced region. The experimental result showed about 40% decrease of insertion rate compared to the blind segmentation method we adopted.

• The Journal of the Acoustical Society of Korea
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• v.13 no.4
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• pp.12-19
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• 1994

This paper reports a new structure for phoneme recognition neural network. The proposed neural network is able to deal with the structure of the frequency bands as well as the temporal structure of phonemic features which used in the conventional TSNN. We trained this neural network using the phonetics (아, 이, 오, ㅅ, ㅊ, ㅍ, ㄱ, ㅇ, ㄹ, ㅁ) and the phoneme recognition of this neural network was a little better than those of conventional TDNN and TSNN using only temporal structure of phonemic features.

• Speech Sciences
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• v.8 no.4
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• pp.99-107
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• 2001

The purpose of this study was to investigate the syllable and phoneme frequencies in the spontaneous speech of some Korean children. Sixty four normally developing children aged from 2 to 5 were involved (male: female=1 : 1, 16 children in each age group). Fifty connected utterances were analyzed using the KCLA (Korean Computerized Language Analysis) 2.0 and Exel. The findings were as follows: 1) /i/ was the most frequently used syllable and was followed by /yo/, /k/, /s'/, /nen/ and so on. 2) The most frequently used Korean phonemes were syllable-initial consonant /k/, syllable- medial vowel /a/ and syllable-final consonant /n/. 3) All seven syllable final consonants (/p,t,k,m,n,n,l/) were used more frequently in the word-medial position than in the word-final position. Three syllable initial consonants(/k, I, s'/) were used more frequently in the word-medial position than in the word-initial position. The syllable and phoneme frequencies in the Korean children's spontaneous speech will provide valuable information in interpreting the severity of phonological disorder and in developing tools for the Korean phonological assessment and intervention.

• Speech Sciences
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• v.8 no.3
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• pp.51-73
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• 2001

This study starts with an investigation of the frequency of allophones from the narrowly transcribed data of (1) most frequently used 2000 words and (2) some passages of standard Seoul Korean. Consequently this entails the investigation of the frequency of phonemes by adding the number of allophones. These two investigations are conducted for the first time in the study of Korean phonology. Previous studies on the reported 'frequency of phoneme' are in fact studies on the 'frequency of letters' and the critical difference between these two types of studies has yet to be clarified accurately. This paper also reveals the proportional distribution of natural classes among Korean phonemes and letters.

• Speech Sciences
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• v.11 no.2
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• pp.193-210
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• 2004

In this paper, we develop a real time lip-synch system that activates 2-D avatar's lip motion in synch with an incoming speech utterance. To realize the 'real time' operation of the system, we contain the processing time by invoking merge and split procedures performing coarse-to-fine phoneme classification. At each stage of phoneme classification, we apply the support vector machine (SVM) to reduce the computational load while retraining the desired accuracy. The coarse-to-fine phoneme classification is accomplished via two stages of feature extraction: first, each speech frame is acoustically analyzed for 3 classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; secondly, each frame is further refined in classification for detailed lip shape using formant information. We implemented the system with 2-D lip animation that shows the effectiveness of the proposed two-stage procedure in accomplishing a real-time lip-synch task. It was observed that the method of using phoneme merging and SVM achieved about twice faster speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed for our method was in the order of 18.22 milliseconds while an HMM method applied under identical conditions resulted about 30.67 milliseconds.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2220-2223
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• 2003

In this paper we used Support Vector Machines(SVMs) recently proposed as the loaming method, one of Artificial Neural Network, to divide continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme divided by eye-measurement. From experiment we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• Journal of Families and Better Life
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• v.26 no.4
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• pp.41-54
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• 2008

The purpose of this study was to investigate predictors concerning preschoolers' ability to read words, in terms of their sub-skills of alphabet knowledge, phonological awareness, and phonological processing. Fourteen literacy sub-tests and three types of reading tasks were administered to 289 kindergartners aged 4 to 6 in Busan. The main results are as follows. Sub-skills that predicted reading ability varied with children's age. Irrespective of children's age groups, knowledge of consonant names and digit naming speed commonly explained the reading of real words. In contrast, skills of syllable deletion and phoneme substitution and knowledge of alphabet composition principles were related to only 4-year-olds' reading skills. Exclusively included was digit memory in predicting 5-year-olds' reading abilities, and knowledge of vowel sounds in 6-year-olds' reading skills. The type of reading task also influenced reading ability. A few common variables such as knowledge of consonant names and vowel sounds, digit naming speed, and phoneme substitution skill explained all types of word reading. Syllable counting skills, however, had predictive value only for the reading of real words. Phoneme insertion skills and digit memory had predictive value for the reading of pseudo words and low frequency letters. Likewise, knowledge of consonant sounds and vowel stroke-adding principles were significant only for the reading of low frequency letters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.224-229
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• 1999

In the field of speech recognition, the research area can be classified into the following two categories: one which is concerned with the development of phoneme-level recognition system, the other with the efficiency of word-level recognition system. The resonable phoneme-level recognition system should detect the phonemic boundaries appropriately and have the improved recognition abilities all the more. The traditional LPC methods detect the phoneme boundaries using Itakura-Saito method which measures the distance between LPC of the standard phoneme data and that of the target speech frame. The MFCC methods which treat spectral transitions as the phonemic boundaries show the lack of adaptability. In this paper, we present new speech recognition system which uses auto-correlation method in the phonemic boundary detection process and the multi-layered Feed-Forward neural network in the recognition process respectively. The proposed system outperforms the traditional methods in the sense of adaptability and another advantage of the proposed system is that feature-extraction part is independent of the recognition process. The results show that frame-unit phonemic recognition system should be possibly implemented.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.110-112
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• 2002

In this paper, a new technique for Thai isolated speech phoneme segmentation is proposed. Based on Thai speech feature, the isolated speech is first divided into low and high frequency components by using the technique of wavelet decomposition. Then the energy contour of each decomposed signal is computed and employed to locate phoneme boundary. To verity the proposed scheme, some experiments have been performed using 1,000 syllables data recorded from 10 speakers. The accuracy rates are 96.0, 89.9, 92.7 and 98.9% for initial consonant, vowel, final consonant and silence, respectively.