• Journal of the Korea Society of Computer and Information
• /
• v.10 no.6 s.38
• /
• pp.47-54
• /
• 2005

In the most of speaker recognition system, speaker's characteristics is extracted from acoustic parameter by speech analysis and we make speaker's reference pattern. Parameters used in speaker recognition system are desirable expressing speaker's characteristics fully and being a few difference whenever it is spoken. Therefore we su99est following to solve this problem. This paper is proposed to use strong spectrum characteristic in non-noise circumstance and prosodic information in noise circumstance. In a stage of making code book, we make the number of data we need to combine spectrum characteristic and Prosodic information. We decide acceptance or rejection comparing test pattern and each model distance. As a result, we obtained more improved recognition rate than we use spectrum and prosodic information especially we obtained stational recognition rate in noise circumstance.

• Journal of the Korea Convergence Society
• /
• v.7 no.6
• /
• pp.7-11
• /
• 2016

Voice recognition systems which use a white noise and voice recognition environment are not correct voice recognition with variable voice mixture. Therefore in this paper, we propose a method using the convergence of Bayesian technique and selecting voice for effective voice recognition. we make use of bank frequency response coefficient for selective voice extraction, Using variables observed for the combination of all the possible two observations for this purpose, and has an voice signal noise information to the speech characteristic extraction selectively is obtained by the energy ratio on the output. It provide a noise elimination and recognition rates are improved with combine voice recognition of bayesian methode. The result which we confirmed that the recognition rate of 2.3% is higher than HMM and CHMM methods in vocabulary recognition, respectively.

• The Journal of the Acoustical Society of Korea
• /
• v.6 no.2
• /
• pp.38-47
• /
• 1987

As Korean language can be phonemically classified according to the characteristic and structure of its pronunciation, Korean syllables can be divided into the phonemes such as consonant and vowel. The divided phonemes are analyzed by using the method of partial autocorrelation, and the order of partial autocorelation coefficient is 15. In analysis, it is shown that each characteristic of the same consonants, vowels, and end consonant in syllables in similar. The experiments is carried out by dividing 675 syllables into consonants, vowels, and end consonants. The recognition rate of consonants, vowels, end-consonants, and syllables are $85.0(\%)$, $90.7(\%)$, $85.5(\%)$and $72.1(\%)$ respectively. In conclusion, it is shown that Korean syllables, divided by the phonemes, are analyzed and recognized with minimum data and short processing time. Furthermore, it is shown that Korean syllables, words and sentences are recognized in the same way.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.8 no.3
• /
• pp.563-568
• /
• 2007

We propose the computation reduction method of real root method that is used in the EVRC(Enhanced Variable Rate Codec) system. The real root method is that if polynomial equations have the real roots, we are able to find those and transform them into LSP. However, this method takes much time to compute, because the root searching is processed sequentially in frequency region. But, the important characteristic of LSP is that most of coefficients are occurred in specific frequency region. So, to reduce the computation time of real root, we used the met scale that is linear below 1kHz and logarithmic above. In order to compare real root method with proposed method, we measured the following two. First, we compared the position of transformed LSP(Line Spectrum Pairs) parameters in the proposed method with these of real root method. Second, we measured how long computation time is reduced. The experimental result is that the searching time was reduced by about 48% in average without the change of LSP parameters.

• Journal of Digital Convergence
• /
• v.13 no.10
• /
• pp.353-358
• /
• 2015

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. In this case, reconstructing the system in order to add or extend a range of vocabulary is a way to solve the problem. This paper propose configured Bhattacharyya algorithm standing by speech recognition learning model using the Bayesian methods which reflect parameter estimation upon the model configuration scalability. It is recognized corrected standard model based on a characteristic of the phoneme using the Bayesian methods for parameter estimation of the phoneme's data and Bhattacharyya algorithm for a similar model. By Bhattacharyya algorithm to configure recognition model evaluates a recognition performance. The result of applying the proposed method is showed a recognition rate of 97.3% and a learning curve of 1.2 seconds.

• Therapeutic Science for Rehabilitation
• /
• v.8 no.4
• /
• pp.53-63
• /
• 2019

Objective : Many communication recovery strategies should be used when communication breakdowns occur for successful communication, however, communication problems increase due to inadequate use of such strategies in older people with dementia. The purpose of this study was to investigate the difference of recovery strategy between dementia and the elderly in conversational discourse. Method : The subjects were eight of Alzheimer's dementia and 10 general elderly. Conversation discourse tasks were conducted face-to-face with the subjects. Communication breakdown and communication recovery strategies were analyzed based on 200 utterances collected in the conversation discourse. Result : First, the AD group had more communication breakdown than the control group, but the recovery rate did not differ between the groups. Second, in the AD group, the nonspecific recovery strategy and the clarification demand strategy were used as the expression strategy. The recovery rate after using expressive strategy was more than 90% in explanation strategy, combined strategy, nonspecific repair strategy, and repetition confirmation strategy. The response strategy used a lot of paraphrase strategy and combined strategies, and the recovery rate after using the response strategy was 100% for the simplification strategy, repeat strategy and gesture strategy. Conclusion : The AD group showed more breakdown of research subjects and breakdown of researchers than control group, and it showed ability to use various expression strategy and response strategy though there was difference in repair rate between communication repair strategy. AD group used nonspecific repair strategy in expression strategy the most and paraphrase strategy in response strategy the most. This shows different characteristic from ordinary elderly people. Therefore, it is necessary to utilize this repair strategy for rehabilitation of AD elderly.