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International Journal of Speech Technology

, Volume 19, Issue 4, pp 805–816 | Cite as

Emotional speech feature normalization and recognition based on speaker-sensitive feature clustering

  • Chengwei Huang
  • Baolin Song
  • Li Zhao
Article

Abstract

In this paper we propose a feature normalization method for speaker-independent speech emotion recognition. The performance of a speech emotion classifier largely depends on the training data, and a large number of unknown speakers may cause a great challenge. To address this problem, first, we extract and analyse 481 basic acoustic features. Second, we use principal component analysis and linear discriminant analysis jointly to construct the speaker-sensitive feature space. Third, we classify the emotional utterances into pseudo-speaker groups in the speaker-sensitive feature space by using fuzzy k-means clustering. Finally, we normalize the original basic acoustic features of each utterance based on its group information. To verify our normalization algorithm, we adopt a Gaussian mixture model based classifier for recognition test. The experimental results show that our normalization algorithm is effective on our locally collected database, as well as on the eNTERFACE’05 Audio-Visual Emotion Database. The emotional features achieved using our method are robust to the speaker change, and an improved recognition rate is observed.

Keywords

Speech emotion recognition Feature normalization Speaker clustering 

Notes

Acknowledgments

This work is partially supported by National Nature Science Foundation of China (No:61231002; No:61273266; No:51075068) and Doctoral Fund of Ministry of Education of China (No:20110092130004).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringSoutheast UniversityNanjingChina

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