• Journal of the Korea Computer Industry Society
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• v.9 no.1
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• pp.21-28
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• 2008

In this paper, the edge parameters were drawn from speaking image around lip and effective automatic lip reading system to recognize the Korean 'a/e/i/o/u' 5 owels were constructed using the parameter. Speaking images around lip were divided into $5{\times}5$ pane. In each pane the number of digital edge element using Sobel operator were evaluated. The observational error between samples was corrected by using normalization method and the normalized value is used for parameter In the experiment to convince the strength of parameter, 50 normal persons were sampled. The images of 10 persons were analyzed and the images of another 40 persons were experimented for recognition. 500 data are gathered and analyzed. Based on this analysis, the neural net system is constructed and the recognition experiments are performed for 400 data. The neural net system gave the best recognition result of 91.1%.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.471-477
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• 2002

Recently, lip-reading has been studied actively as an auxiliary method of automatic speech recognition(ASR) in noisy environments. However, almost of research results were obtained based on the database constructed in indoor condition. So, we dont know how developed lip-reading algorithms are robust to dynamic variation of image. Currently we have developed a lip-reading system based on image-transform based algorithm. This system recognize 22 words and this word recognizer achieves word recognition of up to 53.54%. In this paper we present how stable the lip-reading system is in environmental variance and what the main variant factors are about dropping off in word-recognition performance. For studying lip-reading robustness we consider spatial valiance (translation, rotation, scaling) and illumination variance. Two kinds of test data are used. One Is the simulated lip image database and the other is real dynamic database captured in car environment. As a result of our experiment, we show that the spatial variance is one of degradations factors of lip reading performance. But the most important factor of degradation is not the spatial variance. The illumination variances make severe reduction of recognition rates as much as 70%. In conclusion, robust lip reading algorithms against illumination variances should be developed for using lip reading as a complementary method of ASR.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.478-484
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• 2009

Automatic speech recognition(ASR) is attractive technique in trend these day that seek convenient life. Although many approaches have been proposed for ASR but the performance is still not good in noisy environment. Now-a-days in the state of art in speech recognition, ASR uses not only the audio information but also the visual information. In this paper, We present a novel lip detection method for visual speech recognition in mobile environment. In order to apply visual information to speech recognition, we need to extract exact lip regions. Because eye-detection is more easy than lip-detection, we firstly detect positions of left and right eyes, then locate lip region roughly. After that we apply K-means clustering technique to devide that region into groups, than two lip corners and lip center are detected by choosing biggest one among clustered groups. Finally, we have shown the effectiveness of the proposed method through the experiments based on samsung AVSR database.