Speech based emotion recognition using spectral feature extraction and an ensemble of kNN classifiers

Steven A. Rieger, Rajani Muraleedharan, Ravi P. Ramachandran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Security (and cyber security) is an important issue in existing and developing technology. It is imperative that cyber security go beyond password based systems to avoid criminal activities. A human biometric and emotion based recognition framework implemented in parallel can enable applications to access personal or public information securely. The focus of this paper is on the study of speech based emotion recognition using a pattern recognition paradigm with spectral feature extraction and an ensemble of k nearest neighbor (kNN) classifiers. The five spectral features are the linear predictive cepstrum (CEP), mel frequency cepstrum (MFCC), line spectral frequencies (LSF), adaptive component weighted cepstrum (ACW) and the post-filter cepstrum (PFL). The bagging algorithm is used to train the ensemble of kNNs. Fusion is implicitly accomplished by ensemble classification. The LDC emotional prosody speech database is used in all the experiments. Results show that the maximum gain in performance is achieved by using two kNNs as opposed to using a single kNN.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014
EditorsThomas Fang Zheng, Haizhou Li, Minghui Dong, Jianhua Tao, Yanfeng Lu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages589-593
Number of pages5
ISBN (Electronic)9781479942206
DOIs
StatePublished - Oct 24 2014
Event9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014 - Singapore, Singapore
Duration: Sep 12 2014Sep 14 2014

Publication series

NameProceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014

Other

Other9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014
CountrySingapore
CitySingapore
Period9/12/149/14/14

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Software

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