지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.13 No.4
- 발행연도
- 2019.12
- 수록면
- 131 - 140 (10page)
- DOI
- 10.5626/JCSE.2019.13.4.131
이용수
초록· 키워드
오류제보하기
Through experiment, this research introduces and verifies the usefulness of prosody attributes such as loudness, pitch and sound length as an emotional feature to express the characteristics of the emotion being felt. Sound length is proportional to pronunciation duration and is inversely proportional to the number of phonemic changes per unit of time. Based on these facts, speech speed and the emotional feature were calculated as follows. First, a codebook was generated using mel-frequency cepstral frequency (MFCC) vectors from a preexisting emotional speech database. Second, the MFCC vector of the speech signal was vector-quantized by this codebook to generate a quantized sequence. Third, this sequence was considered for a phoneme sequence and the speech speed was computed by normalizing the number of phoneme changes for each window of time. Fourth, the emotional feature was generated based on this speech speed as follows. The speech speed was added to the MFCC vector with delta and acceleration computation to generate an emotional feature that is related to prosody elements such loudness, pitch and sound length. In order to analyze the utility of these emotional features, a recognition system was developed with the emotional features and a hidden Markov model (HMM). For maximum performance, the degree of MFCC, size of the codebook, method of speech speed computation, window size of speech speed computation, number of HMM model state and the number of the Gaussian mixture model (GMM) per state were carefully selected. To test the recognition system a text-independent, speaker-independent experiment and a text-independent, speaker-dependent experiment were conducted. It was verified that the recognition system using emotional features showed better performance than the recognition system using only speech features, with improvements of 2.5% and 3.5%, respectively, in different experiments.
목차
Abstract
I. INTRODUCTION
II. CHARACTERISTICS OF SPEECH SPEED ACCORDING TO EMOTIONS
III. SUGGESTED ALGORITHM FOR SPEECH SPEED COMPUTATION
IV. EXPERIMENT
V. CONCLUSION
REFERENCES
참고문헌 (18)
참고문헌 신청
R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. G. Taylor, “Emotion recognition in human-computer interaction,” IEEE Signal Processing Magazine, vol. 18, no. 1, pp. 32-80, 2001.
N. S. Kim, “Perspectives on emotion recognition techniques,” Telecommunications Review, vol. 19, no. 5, pp. 773-785, 2009.
P. Pal, A. N. Iyer, and R. E. Yantorno, “Emotion detection from infant facial expressions and cries,” in Proceedings of 2006 IEEE International Conference on Acoustics Speech and Signal Processing, Toulouse, France, 2006, pp. 721-724.
K. Takahashi, “Remarks on emotion recognition from biopotential signals,” in Proceedings of the 2nd International Conference on Autonomous Robots and Agents, Palmerston North, New Zealand, 2004, pp. 1148-1153.
C. E. Williams and K. N. Stevens, “Emotions and speech: some acoustical correlates,” The Journal of the Acoustical Society of America, vol. 52, no. 4B, pp. 1238-1250, 1972.
N. S. Kim, “Perspectives on emotion recognition techniques,” Telecommunications Review, vol. 19, no. 5, pp. 773-785, 2009.
P. Pal, A. N. Iyer, and R. E. Yantorno, “Emotion detection from infant facial expressions and cries,” in Proceedings of 2006 IEEE International Conference on Acoustics Speech and Signal Processing, Toulouse, France, 2006, pp. 721-724.
K. Takahashi, “Remarks on emotion recognition from biopotential signals,” in Proceedings of the 2nd International Conference on Autonomous Robots and Agents, Palmerston North, New Zealand, 2004, pp. 1148-1153.
C. E. Williams and K. N. Stevens, “Emotions and speech: some acoustical correlates,” The Journal of the Acoustical Society of America, vol. 52, no. 4B, pp. 1238-1250, 1972.
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