• Journal of Digital Convergence
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• v.17 no.1
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• pp.433-442
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• 2019

We have developed art and career convergence class for career recognition through drawing a neighborhood job map. Thus, we want to expand the students' career recognition. 3 Classes were conducted in 16 elementary school students from 5th to 6th. Through pre & post-questionnaire, and the interviews with the students' work, job recognition with job value and job prejudice, self-understanding and career attitude were analyzed. As follows. First, students' job recognition was expanded. The ability to identify the needs, values and purposes of jobs has improved. Changes in job values that all jobs are worth have occurred and job bias has changed. Second, the career attitude changed on the basis of self-understanding. Thus, in the context of deciding the job demanded by the society rather than the student's will, art and career convergence class was effective in career recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1047-1054
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• 2015

This paper presents an acoustic model transform method using untranscribed speech database for improved speech recognition. In the presented model transform method, an adapted GMM is obtained by employing the conventional adaptation method, and the most similar Gaussian component is selected from the adapted GMM. The bias vector between the mean vectors of the clean GMM and the adapted GMM is used for updating the mean vector of HMM. The presented GAMT combined with MAP or MLLR brings improved speech recognition performance in car noise and speech babble conditions, compared to singly-used MAP or MLLR respectively. The experimental results show that the presented model transform method effectively utilizes untranscribed speech database for acoustic model adaptation in order to increase speech recognition accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.21-26
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• 2002

This paper presents the new approach of identifying human emotional states such as anger, happiness, normal, sadness, or surprise. This is accomplished by using discrete duration continuous hidden Markov models(DDCHMM). For this, the emotional feature parameters are first defined from input speech signals. In this study, we used prosodic parameters such as pitch signals, energy, and their each derivative, which were then trained by HMM for recognition. Speaker adapted emotional models based on maximum a posteriori(MAP) estimation were also considered for speaker adaptation. As results, the simulation performance showed that the recognition rates of vocal emotion gradually increased with an increase of adaptation sample number.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.119-126
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• 2012

Recently, we used to recognize for human motions with some recognition algorithms. examples, HMM, DTW, PCA etc. In many human motions, we concentrated our research on recognizing fighting motions. In previous work, to obtain the fighting motion data, we used motion capture system which is developed with some active markers and infrared rays cameras and 3 dimension information converting algorithms by the stereo matching method. In this paper, we describe that the different method to acquiring 3 dimension fighting motion data and a HMM algorithm to recognize the data. One of the obtaining 3d data we used is depth map algorithm which is calculated by a stereo method. We test the 3d acquiring and the motion recognition system, and show the results of accuracy and performance results.

• The Journal of the Acoustical Society of Korea
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• v.23 no.8
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• pp.590-596
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• 2004

This paper Presents a new self-adaptation algorithm based on maximum a posteriori (MAP) eigenvoice for Korean connected digit recognition. The proposed MAP eigenvoice is developed by introducing a probability density model for the eigenvoice coefficients. The Proposed approach provides a unified framework that incorporates the Prior model into the conventional eigenvoice estimation. In self-adaptation system we use only one adaptation utterance that will be recognized, we use MAP eigenvoice that is most robust adaptation. In series of self-adaptation experiments on the Korean connected digit recognition task. we demonstrate that the performance of the proposed approach is better than that of the conventional eigenvoice algorithm for a small amount of adaptation data.

• Phonetics and Speech Sciences
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• v.6 no.1
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• pp.77-83
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• 2014

This paper proposes a speaker and environment clustering method in order to overcome the degradation of the speech recognition performance caused by various noise and speaker characteristics. In this paper, instead of using the distance between Gaussian mixture model (GMM) weight vectors as in the Google's approach, the distance between the adapted mean vectors based on the modified maximum a posteriori (MAP) adaptation is used as a distance measure for vector quantization (VQ) clustering. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method yields error rate reduction of 10.6% compared with baseline speaker-independent (SI) model, which is slightly better performance than the Google's approach.

• Journal of Korea Spatial Information System Society
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• v.1 no.1 s.1
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• pp.49-55
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• 1999

Extracting information of maps is necessary to establish the GIS. In this paper, a house recognition method on the scanned topographic map is described. A contour detection method is used to extract houses from the scanned maps and RLE (run-length encoding) method is used for manipulating houses touching grid lines. To handle houses touched to roads and borderlines, morphological operation is used. To remove misrecognition occurred by morphological operation, the legions which contain characters on the map are also automatically eliminated.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.107-113
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• 1997

To achive a robust Korean word recognition system for both channel distortion and additive noise, maximum a posteriori estimation(MAP) adaptation is proposed and the effectiveness of environmental adaptation for improving recognition performance is investigated in this paper. To do this, recognition experiments using MAP adaptation are carried out for the three different speech ; 1) channel distortion is introduced, 2) environmental noise is added, 3) both channel distortion and additive noise are presented. Theeffectiveness of additive feature parameters, such as regressive coefficients and durations, for environmental adaptation are also investigated. From the speaker independent 100 words recognition tests, we had 9.0% of recognition improvement for the case 1), more than 75% for the case 2), and 11%~61.4% for the case 3) respectively, resulting that a MAP environmental adaptation is effective for both channel distorted and noise added speech recognition. But it turned out that duration information used as additive feature parameter did not played an important role in the tests.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.135-135
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• 2003

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2379-2384
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• 2005

We propose a novel method for object recognition using the smart tag system with a stereo vision on a pan-tilt mechanism. We developed a smart tag which included IRED device. The smart tag is attached onto the object. We also developed a stereo vision system which pans and tilts for the object image to be the centered on each whole image view. A Stereo vision system on the pan-tilt mechanism can map the position of IRED to the robot coordinate system by using pan-tilt angles. And then, to map the size and pose of the object for the robot to coordinate the system, we used a simple model-based vision algorithm. To increase the possibility of tag-based object recognition, we implemented our approach by using as easy and simple techniques as possible.