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A Review of Voice Analysis and Recognition Techniques

Journal Name:

  • Indian Streams Research Journal

Publication Year:

  • 2013

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Abstract (Original Language): 
The Speech is the most prominent and primary mode of Communication among human being.Speech recognition and text-to-speech synthesis technologies continue to be adopted successfully by government agencies, industries and research areas. These organizations have typically deployed large enterprise-grade proprietary platforms into their call centers and realized significant business benefits despite the high costs of deploying such technology.This paper addresses about the interaction between the system and the user through voice response.The user can retrieve information using voice effectively whereas the system too gives output using systems voice. The system in turn will notify the user about the current processing that are been carried out by them. It uses Speech recognition to detect the voice from the user and uses the speech control to deliver the voice output.People with disabilities can benefit from speech recognition programs.
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REFERENCES

References: 

I]Rabiner, Lawrence R. and Juang, B.H. (2004).Statistical
Methods for the Recognition and Understanding of
Speech.Rutgers University and the University of California,
Santa Barbara; Georgia Institute of Technology, Atlanta.
II]Rabiner L.R., S.E.L.evinson: (1981) ”Isolated and
connected word recognition – Theory and selected
applications”, IEEE Trans. COM-29, pp.621-629
III] Tolba, H., and O'Shaughnessy, D., (2001). Speech
Recognition by Intelligent Machines, IEEE Canadian
Review (38).
IV] Bridle, J., Deng, L., Picone, J., Richards, H., Ma, J.,
Kamm, T., Schuster, M., Pike, S., Reagan,R.,1998. An
investigation of segmental hidden dynamic models of speech
Co-articulation for automatic speech recognition.Final
Report for the 1998 Workshop on Language Engineering,
Centerfor Language and Speech Processing at John Hopkins
University, pp. 161.
V] Ma, J., Deng, L., 2004. Target-directed mixture linear
dynamic models for spontaneous speech recognition.IEEE
TRANSACTIONS ON SPEECH AND AUDIO
PROCESSING, VOL.12, NO.1, JANUARY 2004.
VI]Stolcke A., Shriberg E., Ferrer L., Kajarekar S., Sonmez
K., Tur G.(2007),“ Speech Recognition As Feature
Extraction For Speaker Recognition” SAFE, Washington
D.C., USA pp 11-13.
VII] Kesarkar M. (2003), “Feature Extraction For Speech
Recogniton” M.Tech. Credit Seminar Report, Electronic
Systems Group, EE. Dep , IIT Bombay.
VIII] Becchetti, C. and Ricotti, L. (2004), “Speech
Recognition Theory and C++ Implementation”, John Wiley
& Sons, Wiley Student Edition, Singapore, pp. 121-188.
IX] [Ballentine99] Ballentine, B., Morgan, D. How to Build
a Speech Recognition Application. A Style Guide for
Telephony Dialogues, CA: Enterprise Integration Group,
Inc., San Ramon. 1999
X] Voice analysis
(http://en.wikipedia.org/wiki/Voice_analysis)
(Accessed on date: June 11, 2013)
XI] Techniques for Feature Extraction in Speech
Recognition System: A Comparative Study.
http://arxiv.org/abs/1305.1145.
(Accessed on date: June 11, 2013)
XII]http://www.en.wikipedia.org(Accessed on date: June
12, 2013)

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