• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.179-186
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• 2005

We can extract spectrum of the voices and analyze those, after employing features of frequency that voices have. In the spectrum of the voices monophthongs are thought to be stable, but when a consonant(s) meet a vowel(s) in a syllable or a word, there is a lot of changes. This becomes the biggest obstacle to phoneme speech recognition. In this study, using Mel Cepstrum and Mel Band that count Frequency Band and auditory information, we analyze the spectrums that each and every consonant and vowel has and the changes in the voices reftects auditory features and make it a system. Finally we are going to present the basis that can segment the voices by an unit of phoneme.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.496-503
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• 2010

Speech recognition system is developed of standalone, In case of a mobile terminal using that low recognition rate represent because of limitation of memory size and audio compression. This study suggest vocabulary recognition highest performance improvement system for separate acoustic search and lexical search. Acoustic search is carry out in mobile terminal, lexical search is carry out in server processing system. feature vector of speech signal extract using GMM a phoneme execution, recognition a phoneme list transmission server using Lexical Tree Search algorithm lexical search recognition execution. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.71%, represent recognition speed of 1.58 second.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.226-230
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• 2016

본 연구는 초등 저학년 읽기부진아동을 위한 한글 파닉스 교육의 기반을 확립하고자 1-2학년 교과서 고빈도 어절 531개를 기반으로 자소 및 음운규칙을 분석하였다. 연구결과, 자소-음소 일치 어절을 기반으로 하였을 때 초성에서 50번 이상 나타난 자소는 /ㄱ/, /ㄹ/, /ㄴ/, /ㅅ/, /ㅎ/, /ㅈ/이다. 중성에서 50번 이상 나타난 자소는 /ㅏ/, /ㅣ/, /ㅗ/, /ㅡ/, /ㅜ/이다. 종성에서 50번 이상 나타난 자소는 /ㄹ/, /ㄴ/, /ㅇ/이다. 자소와 음소가 불일치 된 어절을 기반으로 하였을 때 가장 많이 출현하는 음운규칙은 연음화 규칙이었다. 본 연구결과를 바탕으로 교과서를 기반으로 한 한글 파닉스 교육에 유용하게 사용될 수 있을 것이다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.44-51
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• 1999

This paper presents a massively parallel computational model for the efficient integration of speech and natural language understanding. The phoneme model is based on continuous Hidden Markov Model with context dependent phonemes, and the language model is based on a knowledge base approach. To construct the knowledge base, we adopt a hierarchically-structured semantic network and a memory-based parsing technique that employs parallel marker-passing as an inference mechanism. Our parallel speech recognition algorithm is implemented in a multi-Transputer system using distributed-memory MIMD multiprocessors. Experimental results show that the parallel speech recognition system performs better in recognition accuracy than a word network-based speech recognition system. The recognition accuracy is further improved by applying code-phoneme statistics. Besides, speedup experiments demonstrate the possibility of constructing a realtime parallel speech recognition system.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.194-201
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• 2003

In speech recognition, confidence measuring is to decide whether it can be accepted as the recognized results or not. The confidence is measured by integrating frames into phone and word level. In case of word recognition, the confidence measuring verifies the results of recognition and Out-Of-Vocabulary (OOV). Therefore, the post-processing could improve the performance of recognizer without accepting it as a recognition error. In this paper, we measure the confidence modifying log likelihood ratio (LLR) which was the previous confidence measuring. It accumulates only those which the log likelihood ratio is negative when integrating the confidence to phone level from frame level. When comparing the verification performance for the results of word recognizer with the previous method, the FAR (False Acceptance Ratio) is decreased about 3.49% for the OOV and 15.25% for the recognition error when CAR (Correct Acceptance Ratio) is about 90%.

• The Journal of the Acoustical Society of Korea
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• v.13 no.5
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• pp.51-58
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• 1994

These times many kinds of man-machine Interfaces using speech signal, speech recognizers or speech synthesizers, are proposed and utilized in practice. Especially speech synthesis system is widely used in our life. But its assessment method is still in its first stage. In this paper we propose a method to generate multi-syllable nonsense wordset for the purpose of synthetic speech assessment and applies the wordset to one commercial text-to-speech system. Some results about the experiment is suggested and it is verified that the method to generate a nonsense wordset can be used to assess the intelligibility of the synthesizer in phoneme level or in phonemic environmental level.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.371-377
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• 2003

In this paper, comparison of feature parameters for emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and phonetic feature such as MFCC parameters. In order to evaluate the performance of feature parameters speaker and context independent emotion recognition system was constructed to make experiment. In the experiments, pitch, energy parameters and their derivatives were used as a prosodic information and MFCC parameters and its derivative were used as phonetic information. Experimental results using vector quantization based emotion recognition system showed that recognition system using MFCC parameter and its derivative showed better performance than that using the pitch and energy parameters.

• Journal of Labour Economics
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• v.40 no.3
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• pp.57-77
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• 2017

To prevent the disincentive of labor supply under the current welfare system, we suggest the safety income system, a Korean version of negative income tax. Under the proposed system, for example, a household with four members whose annual income is less than 50 million wons will get financial support from the government. Under the safety income system, labor supply increases and so does the gross domestic product. The disposable income of low-income households increases, which alleviates the income gap among households. Analyzing the Household Income and Expenditure Survey data, we show that under the safety income system the disposable income differentials among households are reduced much more than under the current welfare system or under the universal basic income system.

• The Journal of the Acoustical Society of Korea
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• v.14 no.6
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• pp.54-61
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• 1995

In this paper, we implemented distributed neural network that recognizes phonemes by frame unit for the 30 Korean proverbs sentences consist of 106 isolated words. The features of speech were chosen as PLP cepstrums, energy and zero crossings, where we get those being used as inputs to the distributed neural networks in wide area for a frame to get the good temperal characteristics. A young man of twenties has produced 30 proverbs 5 times. The learning of neural network uses 4 sets of them. 1 set being unused remains for test. There exists silence between words for the easy discrimination. The ratio of frame recognition in large grouping neural network is $95.3\%$ when 4 sets were used for the learning.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.135-140
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• 2015

General CHMM vocabulary recognition system is model observation probability for vocabulary recognition of recognition rate's low. Used as the limiting unit is applied only to some problem in the phoneme model. Also, they have a problem that does not conform to the needs of the search range to meaning of the words in the vocabulary. Performs a phoneme recognition using GMM to improve these problems. We solve the problem according to the limited search words characterized by an improved k-means algorithm. Measure the effectiveness represented by the accuracy and reproducibility as compared to conventional system performance experiments. Performance test results accuracy is 83%p, and recall is 67%p.