Age approximation from speech using Gaussian mixture models

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DC Field	Value	Language
dc.contributor.author	Mittal, T.
dc.contributor.author	Barthwal, A.
dc.contributor.author	Koolagudi, S.G.
dc.date.accessioned	2020-03-30T09:58:39Z	-
dc.date.available	2020-03-30T09:58:39Z	-
dc.date.issued	2013
dc.identifier.citation	Proceedings - 2nd International Conference on Advanced Computing, Networking and Security, ADCONS 2013, 2013, Vol., , pp.74-78	en_US
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/7217	-
dc.description.abstract	In this work, spectral features are extracted from speech to perform speaker classification based on thier age. Mel frequency cepstral coefficients (MFCCs) are explored as features. Gaussian mixture models (GMMs) are proposed as classifiers. The age groups considered in this study are 1-10, 11-20, 21-30, 31-40 and 41-50. The age-group database used in this work is recorded in Hindi from speakers of different ages and dialects containing five Hindi text prompts. The text prompts are constructed using textually neutral Hindi words recorded in neutral emotion which are used for characterizing the age group, for both male and female. Average age recognition performance, in the case of multiple speaker database is observed to be around 92.0%. � 2013 IEEE.	en_US
dc.title	Age approximation from speech using Gaussian mixture models	en_US
dc.type	Book chapter	en_US
Appears in Collections:	2. Conference Papers

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