Earticle

초록

영어

In early research the basic acoustic features were the primary choices for emotion recognition from speech. Most of the feature vectors were composed with the simple extracted pitch-related, intensity related, and duration related attributes, such as maximum, minimum, median, range and variability values. However, researchers are still debating what features influence the recognition of emotion in speech. In this paper, we propose a new method to recognize the emotion from speech signals using fractal dimension features. The fractal feature indicates the non-linearity and self-similarity of a speech signal. For classification and recognition purposes we used the Support Vector Machine technique. In our experiment, a standard database, the Berlin Emotional Speech Database is used as input to measure the effectiveness of our method. By using these features, the obtained results indicated our approach has provided a recognition rate approximate 77%.

목차

Abstract
 1. Introduction
 2. Fractal Features for Emotion Recognition from Speech
 3. Fractal Features Extraction
 4. Emotion Recognition from Speech using Fractal Dimension Features
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Jun-Seok Park [ Dept. of Computer Information & Communication Engineering, Sangmyung University, Korea ]
• Soo-Hong Kim [ Dept. of Computer Software Engineering, Sangmyung University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605