Speech emotion recognition based on entropy of enhanced wavelet coefficients

@article{Sultana2014SpeechER,
  title={Speech emotion recognition based on entropy of enhanced wavelet coefficients},
  author={Sharifa Sultana and Celia Shahnaz and Shaikh Anowarul Fattah and Istak Ahmmed and Jun Yan and M. Omair Ahmad},
  journal={2014 IEEE International Symposium on Circuits and Systems (ISCAS)},
  year={2014},
  pages={137-140}
}
This paper presents a speaker-independent speech emotion recognition method, where emotional features are derived from the Teager energy (TE) operated wavelet coefficients of speech signal. Due to TE operation, the enhanced detail as well as approximate Wavelet coefficients thus obtained is then used to compute entropy. Entropy values of TE operated detail and approximate wavelet coefficients not only reduces feature dimension but also form an effective feature vector for distinguishing… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-7 of 7 references

Meta-classification in acoustic and linguistic feature fusion-based affect recognition

  • B. Schuller, R. J. Villar, G. Riqoll, M. Lang
  • Proc. IEEE Int. Conf. Acoust., Speech Signal…
  • 2005
1 Excerpt

Similar Papers

Loading similar papers…