• MALSORI
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• no.54
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• pp.27-43
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• 2005

Existing standard speech coders can provide speech communication of high quality while they degrade the performance of speech recognition systems that use the reconstructed speech by the coders. The main cause of the degradation is that the spectral envelope parameters in speech coding are optimized to speech quality rather than to the performance of speech recognition. For example, mel-frequency cepstral coefficient (MFCC) is generally known to provide better speech recognition performance than linear prediction coefficient (LPC) that is a typical parameter set in speech coding. In this paper, we propose a speech coder using MFCC instead of LPC to improve the performance of a server-based speech recognition system in network environments. However, the main drawback of using MFCC is to develop the efficient MFCC quantization with a low-bit rate. First, we explore the interframe correlation of MFCCs, which results in the predictive quantization of MFCC. Second, a safety-net scheme is proposed to make the MFCC-based speech coder robust to channel error. As a result, we propose a 8.7 kbps MFCC-based CELP coder. It is shown from a PESQ test that the proposed speech coder has a comparable speech quality to 8 kbps G.729 while it is shown that the performance of speech recognition using the proposed speech coder is better than that using G.729.

• MALSORI
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• no.58
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• pp.45-66
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• 2006

In this paper, a rule-based (transformation-based) NE recognition system is proposed. This system uses Brill's rule inference approach. The performance of the rule-based system and IdentiFinder, one of most successful stochastic systems, are compared. In the baseline case (no punctuation and no capitalisation), both systems show almost equal performance. They also have similar performance in the case of additional information such as punctuation, capitalisation and name lists. The performances of both systems degrade linearly with the number of speech recognition errors, and their rates of degradation are almost equal. These results show that automatic rule inference is a viable alternative to the HMM-based approach to NE recognition, but it retains the advantages of a rule-based approach.

• Journal of muscle and joint health
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• v.20 no.3
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• pp.247-257
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• 2013

Purpose: The purpose of this descriptive study was to investigate the knowledge of radiation protection and the recognition and performance of radiation protection behaviors among perioperative nurses. This study was intended to yield basic data for the development of nursing interventions aimed at improving the nurses' radiation protection behaviors. Methods: One hundred and thirty-seven nurses working in the operating room participated in a survey from September 1 to 30, 2011. The data was analyzed using t-test, ANOVA, and Pearson's correlation with the SPSS/WIN 19.0 program. Results: The average score of radiation protection knowledge was $7.57{\pm}3.45$ out of 16. The average score for the recognition and performance of radiation protection behaviors was $4.32{\pm}0.23$. The knowledge of radiation protection was significantly correlated with the recognition and performance of radiation protection behaviors. Conclusion: Expanding the knowledge of radiation protection could lead to the increase of the recognition and performance of radiation protection behaviors. Therefore, promoting the performance of radiation protection behaviors by improving perioperative nurses' knowledge of radiation protection through reinforcing radiation-related education hereafter could be an important part of nursing.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.26 no.2
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• pp.166-179
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• 2019

Purpose: The purpose of this study was to identify the job performance and educational needs, and recognition of professionalism among care workers, and to organize educational programs according to the priorities of care workers. Methods: The participants were 119 care workers who were working in long-term care facilities. Data were collected from May 31 to June 7, 2019 using self-report questionnaires. Collected data were analyzed using t-tests, ANOVA, and Spearman's Correlation Coefficients. Results: The performance aspects of the job were as follows: care for safety and infection-related, communication and leisure support, and excretion. The most demanded educational needs were in first-aid. Care workers had more than average professional recognition. Job performance and educational needs, and recognition of professionalism differed significantly according to several general characteristics. Conclusions: The educational needs of the areas with low frequency of job performance were high. First-aid is low in frequency, but it is important to cope with emergencies, so it is necessary to continue education. Also, there is a difference in recognition of professionalism according to the career. It will be necessary to develop individualized education programs to meet the needs of care workers.

• Speech Sciences
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• v.10 no.3
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• pp.225-239
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• 2003

In this paper, we develop a keyword spotting system using vocabulary-independent speech recognition technique, and investigate several non-keyword modeling and post-processing methods to improve its performance. In order to model non-keyword speech segments, monophone clustering and Gaussian Mixture Model (GMM) are considered. We employ likelihood ratio scoring method for the post-processing schemes to verify the recognition results, and filler models, anti-subword models and N-best decoding results are considered as an alternative hypothesis for likelihood ratio scoring. We also examine different methods to construct anti-subword models. We evaluate the performance of our system on the automatic telephone exchange service task. The results show that GMM-based non-keyword modeling yields better performance than that using monophone clustering. According to the post-processing experiment, the method using anti-keyword model based on Kullback-Leibler distance and N-best decoding method show better performance than other methods, and we could reduce more than 50% of keyword recognition errors with keyword rejection rate of 5%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.383-386
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• 2002

In this paper, we investigated the pattern recognition performance of the numeric patterns (from 0 to 9) using DNA coding method. The pattern recognition performance of the DNA coding method is compared to the that of the GA(Genetic Algorithm). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by A(Adenine), C(Cytosine), G(Guanine) and T(Thymine), The pattern recognition performance of GA and DNA coding method is evaluated by using the same genetic operators(crossover and mutation) and the crossover probability and mutation probability are set the same value to the both methods. The DNA coding method has better characteristics over genetic algorithms (GA). The reasons for this outstanding performance is multiple possible solution presentation in one string and variable solution string length.

• MALSORI
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• no.53
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• pp.119-127
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• 2005

Speech recognition is one of the user interface technologies in commanding and controlling any terminal such as a TV, PC, cellular phone etc. in a ubiquitous environment. In controlling a terminal, the mismatch between training and testing causes rapid performance degradation. That is, the mismatch decreases not only the performance of the recognition system but also the reliability of that. Therefore, the performance degradation due to the mismatch caused by the change of the environment should be necessarily compensated. Whenever the environment changes, environment adaptation is performed using the user's speech and the background noise of the changed environment and the performance is increased by employing the models appropriately transformed to the changed environment. So far, the research on the environment compensation has been done actively. However, the compensation method for the effect of distant-talking speech has not been developed yet. Thus, in this paper we apply MLLR-based environment adaptation to compensate for the effect of distant-talking speech and the performance is improved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.254-258
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• 2004

In this paper, we propose a robust speech recognition system under noisy environments. Since the presence of noise severely degrades the performance of speech recognition system, it is important to design the robust speech recognition method against noise. The proposed method adopts a new distance measure technique based on stochastic probability instead of conventional method using minimum error. For evaluating the performance of the proposed method, we compared it with conventional distance measure for the 10-isolated Korean digits with car noise. Here, the proposed method showed better recognition rate than conventional distance measure for the various car noisy environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1105-1110
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• 2017

Speech with various emotions degrades the performance of the speaker recognition system. In this paper, a speaker recognition method using emotional adaptation has been proposed to improve the performance of speaker recognition system using affective speech. For emotional adaptation, emotional speaker model was generated from speaker model without emotion using a small number of training affective speech and speaker adaptation method. Since it is not easy to obtain a sufficient affective speech for training from a speaker, it is very practical to use a small number of affective speeches in a real situation. The proposed method was evaluated using a Korean database containing four emotions. Experimental results show that the proposed method has better performance than conventional methods in speaker verification and speaker recognition.

• Journal of Family Resource Management and Policy Review
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• v.12 no.2
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• pp.73-93
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• 2008

The purpose of this study was to analyze the effects of workers' human resource development and their recognition of human resource on-the-job satisfaction. A sample of 4,727 workers that was selected from Korea Labor Panel Data was analyzed by t-test and multiple regression, and was tested by causal effects among related variables. The major findings were as follows: First, the workers' recognition of their job performance level vs. educational attainment was affected by their annual income, job status, educational attainment, gender, and experiences of human resource development. Second, the workers' job satisfaction was affected by gender, age, educational attainment, health status, job status, annual income, experiences of human resource development, recognition of their job performance level vs. educational attainment, and recognition for their job availability. Third, the factors that had a causal effect on workers' job satisfaction were educational attainment, gender, age, health status, annual income, and experiences of human resource development. Above all, workers' educational attainment had a strong direct effect on job satisfaction, and annual income had a strong indirect effect on it. From these findings, it can be concluded that workers' effort and trial for development and investment of human resource played an important role in increasing job satisfaction.