NDLI: Speech Emotion Recognition Using Canonical Correlation Analysis and Probabilistic Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Ling Cen Wee Ser Zhu Liang Yu
Copyright Year	2008
Description	Author affiliation: Centre for Signal Process., Nanyang Technol. Univ., Singapore, Singapore (Wee Ser; Zhu Liang Yu) \|\| A *STAR, Inst. for Infocomm Res., Singapore, Singapore (Ling Cen)
Abstract	In this paper, automatic identification of emotional states from human speech is addressed. While several papers have been published in the literature on speech emotion recognition, the features used are taken or modified from those used for speech recognition purposes. However, not all features used for speech recognition are of equal importance for emotion recognition. This paper addresses this issue and proposes a systematic method on feature selection for emotion recognition from speech signals. The idea is to work on a well-selected small feature set and use it to remove irrelevant information. Specifically, the proposed method uses the similar idea of the Canonical Correlation Analysis (CCA) to estimate the linear relationship between the various features and the emotional states. The outcome is a set of features that are of most relevance to the emotions. Experiments have been conducted using the LDC database and with the use of the Probabilistic Neural Network (PNN) as the classification method. The results obtained show that, comparable accuracies can be obtained for the emotional states tested with the use of only about 30% of the features considered. This implies that the computational load can be reduced greatly too.
Starting Page	859
Ending Page	862
File Size	115843
Page Count	4
File Format	PDF
ISBN	9780769534954
DOI	10.1109/ICMLA.2008.85
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Emotion recognition Speech analysis Feature selection Humans Spatial databases classification Neural networks Speech recognition Machine learning Feature extraction Speech Emotion Speech processing State estimation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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