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열악한 환경에 강인한 화자인증을 위한 위상 기반 특징 추출 기법
권철홍,Kwon, Chul-Hong 한국정보통신학회 2010 한국정보통신학회논문지 Vol.14 No.3
Additive noise and channel distortion strongly degrade the performance of speaker verification systems, as it introduces distortion of the features of speech. This distortion causes a mismatch between the training and recognition conditions such that acoustic models trained with clean speech do not model noisy and channel distorted speech accurately. This paper presents a phase-related feature extraction method in order to improve the robustness of the speaker verification systems. The instantaneous frequency is computed from the phase of speech signals and features from the histogram of the instantaneous frequency are obtained. Experimental results show that the proposed technique offers significant improvements over the standard techniques in both clean and adverse testing environments. 화자인증 시스템은 훈련 환경과 인식 환경이 다른 경우 인식 성능이 크게 저하된다. 이러한 훈련과 인식 환경의 불일치는 다양한 잡음과 상이한 채널 환경 때문이다. 본 논문은 화자인증 시스템의 강인성 개선을 위하여 음성신호의 위상에 기반한 특정 추출 기법을 제안한다. 이 방법은 음성신호의 위상으로부터 순시 주파수를 계산하여 대역별로 순시 주파수를 모두 모아 구한 히스토그램으로부터 특징 계수를 추출한다. 이 특징 파라미터를 적용한 결과 조 용한 환경뿐만 아니라 잡음환경 그리고 채널 왜곡 환경에서도 화자인증 시스템의 성능이 개선됨을 알 수 있다.
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권철홍,강효원,이상필,Kwon Chul-Hong,Kang Hyo-Won,Lee Sang-Pil 대한음성학회 2003 말소리 Vol.48 No.-
An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. In this paper we propose an HMM based speech recognizer which automatically classifies pronunciation errors when Korean speak Japanese. For this purpose we also develop phoneme recognizers for Korean and Japanese. Experimental results show that the machine scores of the proposed recognizer correlate with expert ratings well.
히스토그램 변환에서 기준분포의 표준편차 변경에 따른 강인한 화자인증 성능 개선
권철홍(Kwon Chul Hong) 한국음성학회 2010 말소리와 음성과학 Vol.2 No.3
Additive noise and channel mismatch strongly degrade the performance of speaker verification systems, as they distort the features of speech. In this paper a histogram transformation technique is presented to improve the robustness of text-independent speaker verification systems. The technique transforms the features extracted from speech such that theirhistogram is conformed to a reference distribution. The effect of different standard deviations for the reference distribution is investigated. Experimental results indicate that, in channel mismatched environments, the proposed technique offers significant improvements over existing techniques. We also verify performance improvement of the proposed method using statistics.
단순작업으로 인한 정신피로도 측정을 위한 음성기술을 이용한 CART 기반 진단모델
권철홍(Kwon, Chul Hong) 한국음성학회 2016 말소리와 음성과학 Vol.8 No.4
This paper presents a CART(Classification and Regression Tree)-based model to diagnose mental fatigue using speech technology. The parameters used in the model are the significant speech parameters highly correlated to the fatigue and questionnaire responses obtained before and after imposing the fatigue. It is shown from the experiments that the proposed model achieves classification accuracies of 96.67% and 98.33% using the speech parameters and questionnaire responses, respectively. This implies that the proposed model can be used as a tool to diagnose the mental fatigue, and that speech technology is useful to diagnose the fatigue.
권철홍 ( Chul Hong Kwon ),김종열 ( Jong Yeol Kim ),김근호 ( Keun Ho Kim ),한성만 ( Sung Man Han ) 대전대학교 한의학연구소 2011 한의학연구소 논문집 Vol.19 No.2
Objective: Sasang constitution medicine utilizes voice characteristics to diagnose a person`s constitution. In this paper we propose methods to analyze Sasang constitution using speech information technology. That is, this study aims at establishing the relationship between Sasang constitutions and their corresponding voice characteristics by investigating various speech variables. Materials & Methods: Voice recordings of 1,406 speakers are obtained whose constitutions have been already diagnosed by the experts in the fields. A total of 144 speech features obtained from five vowels and a sentence are used. The features include pitch, intensity, formant, bandwidth, MDVP and MFCC related variables for each constitution. We analyze the speech variables and find whether there are statistically significant differences among three constitutions. Results: The main speech variables classifying three constitutions are related to pitch and MFCCs for male, and formant and MFCCs for female. The correct decision rate is 73.7% for male Soeumin, 63.3% for male Soyangin, 57.3% for male Taeumin, 74.0% for female Soeumin, 75.6% for female Soyangin, 94.3% for female Taeumin, and 73.0% on the average. Conclusion: Experimental results show that statistically significant correlation between some speech variables and the constitutions is observed.
권철홍(Chul hong Kwon),박선(Sun Park) 한국자동차공학회 1987 오토저널 Vol.9 No.1
<br/> The driving pattern was studied in Seoul along nineteen representative routes using a test car equipped with all the instruments required for recording traffic flow and measuring fuel consumption. Speed histories, gear shift points, instantaneous fuel consumption rates, etc. were recorded and the data were anlyzed to determine the traffic characteristics for Seoul.<br/> The Seoul-14 Mode has been developed to simulated actual driving conditions in Seoul with respect to fuel consumption. The average speed of the Seoul-14 Mode is 30.1 Km/h and the Mode length is 11.94 Km.
이수화,권철홍,Lee, Soo Hwa,Kwon, Chul Hong 국제문화기술진흥원 2022 The Journal of the Convergence on Culture Technolo Vol.8 No.6
Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.
최재길,권철홍,Choi, Jae-Kil,Kwon, Chul-Hong 대한음성학회 2007 말소리 Vol.63 No.-
It is well known that when there is an acoustic mismatch between the speech obtained during training and testing, the accuracy of speaker verification systems drastically deteriorates. This paper presents the use of MFCCs' histogram enhancement technique in order to improve the robustness of a speaker verification system. The technique transforms the features extracted from speech within an utterance such that their statistics conform to reference distributions. The reference distributions proposed in this paper are uniform distribution and beta distribution. The transformation modifies the contrast of MFCCs' histogram so that the performance of a speaker verification system is improved both in the clean training and testing environment and in the clean training and noisy testing environment.