DOI QR코드

DOI QR Code

Emotion Recognition Based on Frequency Analysis of Speech Signal

  • Sim, Kwee-Bo (School of Electronic Engineering, Chung-Ang University) ;
  • Park, Chang-Hyun (School of Electronic Engineering, Chung-Ang University) ;
  • Lee, Dong-Wook (School of Electronic Engineering, Chung-Ang University) ;
  • Joo, Young-Hoon (School of Electronic and Information Engineering, Kunsan National University)
  • 발행 : 2002.06.01

초록

In this study, we find features of 3 emotions (Happiness, Angry, Surprise) as the fundamental research of emotion recognition. Speech signal with emotion has several elements. That is, voice quality, pitch, formant, speech speed, etc. Until now, most researchers have used the change of pitch or Short-time average power envelope or Mel based speech power coefficients. Of course, pitch is very efficient and informative feature. Thus we used it in this study. As pitch is very sensitive to a delicate emotion, it changes easily whenever a man is at different emotional state. Therefore, we can find the pitch is changed steeply or changed with gentle slope or not changed. And, this paper extracts formant features from speech signal with emotion. Each vowels show that each formant has similar position without big difference. Based on this fact, in the pleasure case, we extract features of laughter. And, with that, we separate laughing for easy work. Also, we find those far the angry and surprise.

키워드

참고문헌

  1. C. H. Park, D. W. Lee, Y. H. Joo, and K. B. Sim, 'Detecting data which represent emotion features from the speech signal,' ICCAS 2001, Che-ju island, Korea, fall, 2001
  2. F. Dellaert, T. Pozin, and A. Waibel, Recognizing Emotion In Speech, Technical Report, Carnegie Mellon Univ
  3. K. S. Lee and D. I. Seok, Auditory, Dae-Gu University, 1996
  4. L. Rabiner and B. H. Juang, Fundamentals of speech recognition, Prentice-Hall International, 1993
  5. T. L. New and F. S. Wei, Speech Based Emotion Classification, EIectrical and Electronic Technology, Tencon, 2001
  6. X. Lin, Y. Chen, S. Lim, and C.Lim, Recognition of Emotional State from Spoken Sentences, MuItimedia Signal Processing, IEEE 3rd workshop, 1999

피인용 문헌

  1. The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech vol.14, pp.9, 2014, https://doi.org/10.3390/s140916692
  2. Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition vol.15, pp.1, 2015, https://doi.org/10.3390/s150101458