목소리 특성과 음성 특징 파라미터의 상관관계와SVM을 이용한 특성 분류 모델링
Correlation analysis of voice characteristics and speech feature parameters, and classification modeling using SVM algorithm
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초록
This study categorizes several voice characteristics by subjective listening assessment, and investigates correlation between voice characteristics and speech feature parameters. A model was developed to classify voice characteristics into the defined categories using SVM algorithm. To do this, we extracted various speech feature parameters from speech database for men in their 20s, and derived statistically significant parameters correlated with voice characteristics through ANOVA analysis. Then, these derived parameters were applied to the proposed SVM model. The experimental results showed that it is possible to obtain some speech feature parameters significantly correlated with the voice characteristics, and that the proposed model achieves the classification accuracies of 88.5% on average.
Abstract
This study categorizes several voice characteristics by subjective listening assessment, and investigates correlation between voice characteristics and speech feature parameters. A model was developed to classify voice characteristics into the defined categories using SVM algorithm. To do this, we extracted various speech feature parameters from speech database for men in their 20s, and derived statistically significant parameters correlated with voice characteristics through ANOVA analysis. Then, these derived parameters were applied to the proposed SVM model. The experimental results showed that it is possible to obtain some speech feature parameters significantly correlated with the voice characteristics, and that the proposed model achieves the classification accuracies of 88.5% on average.
참고문헌 (19)
- 방재훈, "프롬프트 레이블링을 이용한 적응형 음성기반 감정인식 프레임워크", 정보과학회 컴퓨팅의 실제 논문지, 21(2), pp.160~165 (2015), 한국정보과학회
- Rahman, T., "A personalized emotion recognition system using an unsupervised feature adaptation scheme", Proceedings of International Conference on the Acoustics, Speech and Signal Processing, pp.5117~5120 (2012)
- 권철홍, "감정 인식을 위한 음성 특징 도출", 말소리와 음성과학, 4(2), pp.73~78 (2012), 한국음성학회
- 김정인, "정신피로와 음성특징과의 상관관계 측정", 말소리와 음성과학, 6(2), pp.3~8 (2014), 한국음성학회[DOI]
- 김태훈, "육체피로와 음성신호와의 상관관계", 말소리와 음성과학, 7(1), pp.11~17 (2015), 한국음성학회[DOI]
- (2016)[URL]
- "MDVP: Multi Dimensional Voice Program, KayPentax"[URL]
- Shue, Y., "VoiceSauce:a program for voice analysis", Proceedings of the 17th International Congress of Phonetic Sciences, pp.1846~1849 (2011)
- 한성만, "목소리 특성의 주관적 평가와 음성 특징과의 상관관계 기초연구", 말소리와 음성과학, 3(4), pp.85~91 (2011), 한국음성학회
- AMOS 통계분석방법, "논문작성에 필요한 SPSS", pp.2015, 송지준
- "IBM SPSS statistics"[URL]
- Chang, C., "LIBSVM: a library for support vector machines", ACM Transaction on Intelligent Systems and Technology, 2(3), pp.1~27 (2011)
- 김태훈, "음성 특징 파라미터를 이용한 SVM 기반 육체피로도 진단모델", 말소리와 음성과학, 8(2), pp.17~22 (2016), 한국음성학회[DOI]
- Alippi, C., "Virtual k-fold cross validation: An effective method for accuracy assessment", The 2010 International Joint Conference on Neural Networks (IJCNN), pp.18~23 (2010)
- Ferrand, C., "Harmonics-to-Noise Ratio: an index of vocal aging", Journal of Voice, 16(4), pp.480~487 (2002)
- Boersma, P., "Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound", Proceedings of Institute of Phonetic Sciences, 17, pp.97~110 (1993)
- Hillenbrand, J., "Acoustic correlates of breathy vocal quality : dysphonic voices and continuous speech", Journal of Speech and Hearing Research, 39, pp.311~321 (1996)
- Linville, S., "Source characteristics of aged voice assessed from long-term average spectra", Journal of Voice, 16(4), pp.472~479 (2002)
- Mendoza, E., "Differences in voice quality between men and women : use of the long-term average spectrum(LTAS)", Journal of Voice, 10(1), pp.59~66 (1996)
