Abstract
In general, self help systems are being increasingly deployed by service based industries because they are capable of delivering better customer service and increasingly the switch is to voice based self help systems because they provide a natural interface for a human to interact with a machine. A speech based self help system ideally needs a speech recognition engine to convert spoken speech to text and in addition a language processing engine to take care of any misrecognitions by the speech recognition engine. Any off-theshelf speech recognition engine is generally a combination of acoustic processing and speech grammar. While this is the norm, we believe that ideally a speech recognition application should have in addition to a speech recognition engine a separate language processing engine to give the system better performance. In this paper, we discuss ways in which the speech recognition engine and the language processing engine can be combined to give a better user experience.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Daniel Hong, “An introductory guide to speech recognition solutions,” Industry white paper by Datamonitor, 2006.
Microsoft Research, “Microsoft speech - solutions: Password reset,” http://www.microsoft.com/speech/solutions/pword/default.mspx, 2007.
Tellme, “Every day info,” http://www.tellme.com/products/TellmeByVoice, 2007.
Roberto Pieraccini and Chin-Hui Lee, “Factorization of language constraints in speech recognition,” in Proceedings of the 29th annual meeting on Association for Computational Linguistics, Morristown, NJ, USA, 1991, pp. 299–306, Association for Computational Linguistics.
S. L. Young, A. G. Hauptmann, W. H. Ward, E. T. Smith, and P. Werner, “High level knowledge sources in usable speech recognition systems,” Commun. ACM, vol. 32, no. 2, pp. 183–194, 1989.
Dirk Buhler, Wolfgang Minker, and Artha Elciyanti, “Using language modelling to integrate speech recognition with a flat semantic analysis,” in 6th SIGdial Workshop on Discourse and Dialogue, Lisbon, Portugal, September 2005.
Victor W. Zue, James Glass, David Goodine, Hong Leung, Michael Phillips, Joseph Polifroni, and Stephanie Seneff, “Integration of Speech Recognition and Natural Language Processing in the MIT Voyager System,” in Proc. ICASSP, 1991, vol. 1, pp. 713–716.
Ye-Yi Wang, Alex Acero, Milind Mahajan, and John Lee, “Combining statistical and knowledge-based spoken language understanding in conditional models,” in Proceedings of the COLING/ACL on Main conference poster sessions, Morristown, NJ, USA, 2006, pp. 882–889, Association for Computational Linguistics.
Sunil Kumar Kopparapu, Akhlesh Srivastava, and P. V. S. Rao, “Minimal parsing key concept based question answering system,” in HCI (3), Julie A. Jacko, Ed. 2007, vol. 4552 of Lecture Notes in Computer Science, pp. 104–113, Springer.
Microsoft, “Microsoft Speech API,” http://msdn.microsoft.com/enus/library/ms723627(VS.85).aspx, Accessed Nov 2008.
Open Source, “Kannel: Open source WAP and SMS gateway,” http://www.kannel.org/, Accessed Nov 2008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this paper
Cite this paper
Kopparapu, S.K. (2010). Voice Based Self Help System: User Experience Vs Accuracy. In: Sobh, T. (eds) Innovations and Advances in Computer Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3658-2_18
Download citation
DOI: https://doi.org/10.1007/978-90-481-3658-2_18
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-3657-5
Online ISBN: 978-90-481-3658-2
eBook Packages: EngineeringEngineering (R0)