Speech Emotion Recognition: A Review
Rahul. B. Lanjewar1, D. S. Chaudhari2

1Rahul. B. Lanjewar, M.Tech IIIrd Sem.Student, Department of Electronic System & Communication, Government College of Engineering, Amravati (Maharashtra), India.
2Dr. D. S. Chaudhari, Head, Department of Electronics & Telecommunication, Government College of Engineering, Amravati (Maharashtra), India.
Manuscript received on 12 March 2013 | Revised Manuscript received on 21 March 2013 | Manuscript Published on 30 March 2013 | PP: 68-71 | Volume-2 Issue-4, March 2013 | Retrieval Number: D0524032413/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The man-machine relation has demanded the smart trends that machines have to react after considering the human emotional levels. The technology boost improved the machine intelligence that it gained the capability to identify human emotions at expected level. Harnessing the approaches of signal processing and pattern recognition algorithms a smart and emotions specific man-machine interaction can be achieved with the tremendous scope in the field of automated home as well as commercial applications. This paper reviews the aspects of speech prosody in the form of pitch, intensity, speaking rate at the same the contribution of Mel Frequency Cepstrum Coefficients based speech features in speech emotion recognition implementation. The impact of incorporating fusion techniques, wavelet domain analysis and the classifier models on the recognition rate in the identification of six emotional categories namely happy, angry, neutral, surprised, fearful and sad from the standard speech database is emphasized with intend to improve recognition fidelity.
Keywords: Features, Emotion, MFCC, HMM, Classifier, Database, Fusion.

Scope of the Article: Pattern Recognition