Title | ||
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Emotional speech feature normalization and recognition based on speaker-sensitive feature clustering. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2016 | 10.1007/s10772-016-9371-3 | International Journal of Speech Technology |
Keywords | Field | DocType |
Speech emotion recognition, Feature normalization, Speaker clustering | Feature vector,Normalization (statistics),Pattern recognition,Feature (computer vision),Computer science,Speech recognition,Speaker recognition,Feature (machine learning),Artificial intelligence,Linear discriminant analysis,Cluster analysis,Mixture model | Journal |
Volume | Issue | ISSN |
19 | 4 | 1572-8110 |
Citations | PageRank | References |
2 | 0.41 | 12 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Chengwei Huang | 1 | 20 | 3.81 |
Baolin Song | 2 | 2 | 0.75 |
Li Zhao | 3 | 198 | 22.70 |