Emotion Detection based on the Hidden Markov model Chain Speech Recognition
- 1Department of Computer Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
- 2Department of Computer Engineering, Hendijan Branch, Islamic Azad University, Hendijan, Iran
Res. J. Recent Sci., Volume 5, Issue (5), Pages 47-52, May,2 (2016)
Detection of user mode is one of the main arguments used in all systems such as expert systems, as a significant Parameter. Hidden Markov model is one of the most important models in speech recognition, that the several strong researches confirmed this method. This research attempts to recognize person’s voice based on the mathematical model of the emotional detection by recognizing its voice.
- Van Den Broek E. and Westerin J. (2009)., Considerations for emotion-aware consumer products., Elsevier Applied Ergonomics, 40(6).
- Hong J., Yang S. and Cho S. Cona (2010)., MSN: A context-aware messenger using dynamic Bayesian networks with wearable sensors., Elsevier Expert Systems with Applications, 37(6).
- Yoo H., Kim M., Kwon O. (2011). Emotional index measurement method for context-aware service, Elsevier Expert Systems with Applications, 38(1) (2011), undefined
- Rabiner L. and Biing Hwang J. (2001)., Fundamentals of Speech Recognition., ISBN-13: 978-0130151575, 69-139.
- Alpaydin E. (2010)., Introduction to Machine Learning., The MIT Press, ISBN-13: 978-0-262-01211-9.
- Huang X., Acero A. and Hon H.W. (2001)., Spoken Language Processing: A Guide to Theory, Algorithm and System Development. ISBN-13: 978-0130226167.
- Loke S. (2006)., Context-Aware Pervasive Systems: Architectures for a New Breed of Applications., ISBN-13: 978-0849372551.
- Martin T.B., Nelson A.L. and Zadell H.J. (1964)., Speech recognition by feature abstraction techniques., Tech. Report AL-TDR-64-176, Air Force Avionics Lab.
- Mc Quaid H., Goel A. and McManus M. (2003)., Designing for a pervasive information environment: The importance of information architecture., Conference HCI, Designing for Society Bath, UK, Proceedings Volume 2.
- Myers C.S. and Rabiner L.R. (1981)., A level building dynamic time wraping algorithm for connected word recognition., IEEE Trans. Acoustics, Speech, Signal Proc., ASSP-29: 284-297.
- Nagata K., Kato Y. and Chiba S. (1963)., Spoken digit recognizer for Japanese language., NEC Res. Develpo., No. 6.
- Olson H.F. and Belar H. (1975)., Phonetic Typewriter., J. Acoust. Soc. Am., 28(6).
- Paul D.B. (1989)., The Lincoln robust continuous speech recognizer., ICASSP 89, Glasgow, Scotland, 449-452.
- Górriz J.M., Ramírez J., Lang E.W., Puntonet C.G. and Turias I. (2010)., Improved likelihood ratio test based voice activity detector applied to speech recognition. Elsevier Speech Communication, 52(7)., undefined
- Rabiner L.R. (1989)., A tutorial on Hidden Markov Models and selected applications in speech recognition., IEEE, 77(2)
- Rabiner L.R., Levinson S.E., Rosenberg A.E. and Wilpon J.G. (1979)., Speaker independent recognition of isolated words using clustering techniques., IEEE Trans. Acoustics, Speech, Signal Proc., ASSP-27, 336-349.
- Rastegari E., Rahmani A.M. and Setayeshi S. (2008)., Pervasive Computing In Healthcare Systems., International Journal of Biometrics and Bioinformatics (IJBB), 3(4).
- Reddy D.R. (1966)., An approach to computer speech recognition by direct analysis of the speech wave., Tech. Report No. C549, Computer Science Dept., Stanford Univ.
- Sakai T. and Doshita S. (1962)., The phonetic typewriter, information processing, IFIP Congress, Munich.
- Sakoe H. (1979)., Two level DP matching—A dynamic programming based pattern matching algorithm for connected word recognition., IEEE Trans. Acoustics, Speech, Signal Proc., ASSP-27: 588-595.
- Sakoe H. and Chiba S. (1978)., Dynamic programming algorithm optimization for spoken word recognition., IEEE Trans. Acoustics, Speech, Signal Proc. ASSP26 (1).
- Suzuki J. and Nakata K. (1961)., Recognition of Japanese vowels: Preliminary to the recognition of speech., J. Radio Res. Lab, 37(8).
- Tappert C.C., Dixon N.R., Rabinowitz A.S. and Chapman W.D. (1971)., Automatic recognition of continuous speech utilizing dynamic segmentation, dual classification, sequential decoding and error recovery., Rome Air Dev. Cen, Rome, NY, Tech Report TR-71-146.
- Amano A., Aritsuka T., Hataoka N. and Ichikawa A. (1989)., On the use of neural networks and fuzzy logic in speech recognition., Int. Joint Conf. Neural Networks, (301).
- Van Kleek M.K. (2003)., Info: An Architecture for Smart Billboards for Informal Public Spaces., UBICOMP, Seattle, WA.
- Weintraub M. et al. (1989)., Linguistic constraints in Hidden Markov Model based speech recognition, ICASSP 89, Glasgow, Scotland, 699-702.
- Bridle J.S. and Brown M.D. (1979)., Connected word recognition using whole word templates., Inst. Acoust. Autumn Conf, (25).
- Zimmermann H.J. (1996)., Fuzzy set theory and its applications., Kluwer Academic Publishers. Boston/Dordrecht/London, Third edition.
- Zue V., Glass J., Phillips M. and Seneff S. (1989)., The MIT summit speech recognition system: A progress report., DARPA Speech and Natural Language workshop, 179-189.
- Matiko J.W., Beeby S.P. and Tudor J. (2014)., Real time emotion detection within a wireless sensor network and its impact on power consumption., IEEE Wireless Sensor Systems, IET, 4(4).
- Munezero M., Montero C.S., Sutinen E. and Pajunen J. (2014)., Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text., 5(2).
- Yeh Huann Goh, Raveendran P. and Jamuar S.S. (2014)., Robust speech recognition using harmonic features., IEEE Signal Processing, IET8(2)
- Wand M., Janke M. and Schultz T. (2014)., Tackling Speaking Mode Varieties in EMG-Based Speech Recognition., IEEE Biomedical Engineering, IEEE Transactions on, 61(10).