Abstract
Sadness, one of the negative emotions, may cause undesirable impact to the daily life. Therefore, it is desirable to automatically detect sadness emotion in human-machine interactions in order to adopt measures to impair the negative effects caused by it. Speech is one of the means used by human to express emotions, therefore, it is reasonable to detect sadness emotion using speech samples. In this paper, we analyzed relevant speech features, and proposed an improved Back Propagation (BP) network for sadness recognition. The experimental results show that the improved BP network proposed has better performance than traditional BP networks in detecting sadness emotion.
Publisher
Trans Tech Publications, Ltd.
Reference11 articles.
1. Tao, Jianhua; Tieniu Tan. Affective Computing: A Review. Affective Computing and Intelligent Interaction. LNCS 3784. Springer. 981–995. (2005).
2. Isen, A. M.: Positive affect and decision making. In M. Lewis & J. Haviland-Jones (Eds. ), Handbook of Emotions (2nd ed. ). New York: Guilford. p.417–435. (2000).
3. Ase Innes-Ker, Paula M. Niedenthal. Emotion concepts and emotional states in social judgment and categorization. Journal of Personality and Social Psychology, Vol. 83, No. 4, 804–816. (2002).
4. Tin Lay New, Say Wei Foo, Liyangage C. De Silva. Speech emotion recognition using hiden Markov models. Speech Communication, 41: pp.603-623. (2003).
5. L.Q. Fu, X. Mao, L.J. Chen. Speaker Independent Emotion Recognition Based SVM/HMMs Fusion System. IEEE Proceeding of ICALIP , pp.61-65. (2008).
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献