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

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 extr...

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Bibliographic Details
Published in:International journal of speech technology Vol. 19; no. 4; pp. 805 - 816
Main Authors: Huang, Chengwei, Song, Baolin, Zhao, Li
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2016
Springer Nature B.V
Subjects:
Acknowledgment
Acoustics
Algorithms
Artificial Intelligence
Classifiers
Cluster analysis
Clustering
Discriminant analysis
Emotion recognition
Emotions
Engineering
Feature extraction
Feature recognition
Fuzzy
Normalization
Principal components analysis
Probabilistic models
Signal,Image and Speech Processing
Social Sciences
Speech
Speech recognition
Speeches
Utterances
Vector quantization
Voice recognition
Feature normalization
Speech emotion recognition
Speaker clustering
ISSN:1381-2416, 1572-8110
Online Access:Get full text
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Summary: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.
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ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-016-9371-3