Kinkiri, Saritha, Melis, Wim J.C. ORCID: 0000-0003-3779-8629 and Keates, Simeon ORCID: 0000-0002-2826-672X (2017) Machine learning for voice recognition. In: The Second Medway Engineering Conference on Systems: Efficiency, Sustainability and Modelling, Tuesday, 6th June 2017, University of Greenwich, Chatham Maritime ME4 4TB.

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Abstract

Verbal communication is very important to us humans, but using thisperforming verbal communication to communicateion with machines still faces particular challenges. Therefore, researchers are trying to find ways to make communication with a machine more similar to communicating with other people, for which two systems have been identified: speech and voice recognition. While speech recognition has aimed to become speaker independent, voice recognition focuses on identifying the speaker, by looking at the tone of the voice, which is affected by the physical characteristics of that person. This requires one to identify these unique tonal features, to then train a system with this data. Being able to perform this identification well, would also bring benefit to speech recognition by allowing the system to adjust to the characteristics of that speaker and how he/she produces their sounds.

Item Type:	Conference or Conference Paper (Paper)
Uncontrolled Keywords:	Machine learning, Communication, Voice recognition, Speech recognition, Security, Biometric authentication
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG) Faculty of Engineering & Science > Future Technology and the Internet of Things
Last Modified:	24 Apr 2018 12:55
URI:	http://gala.gre.ac.uk/id/eprint/17425

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