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Hämäläinen, A., Meinedo, H., Tjalve, M., Pellegrini, T., Trancoso, I. & Dias, M. S. (2014). Improving speech recognition through automatic selection of age group – specific acoustic models. In Jorge Baptista, Nuno Mamede, Sara Candeias, Ivandré Paraboni, Thiago A. S. Pardo, Maria das Graças Volpe Nunes (Ed.), Computational Processing of the Portuguese Language. PROPOR 2014. Lecture Notes in Computer Science. (pp. 12-23). Cham: Springer.

A. Hämäläinen et al.,  "Improving speech recognition through automatic selection of age group – specific acoustic models", in Computational Processing of the Portuguese Language. PROPOR 2014. Lecture Notes in Computer Science, Jorge Baptista, Nuno Mamede, Sara Candeias, Ivandré Paraboni, Thiago A. S. Pardo, Maria das Graças Volpe Nunes, Ed., Cham, Springer, 2014, vol. 8775, pp. 12-23

@incollection{hämäläinen2014_1716157689736,
	author = "Hämäläinen, A. and Meinedo, H. and Tjalve, M. and Pellegrini, T. and Trancoso, I. and Dias, M. S.",
	title = "Improving speech recognition through automatic selection of age group – specific acoustic models",
	chapter = "",
	booktitle = "Computational Processing of the Portuguese Language. PROPOR 2014. Lecture Notes in Computer Science",
	year = "2014",
	volume = "8775",
	series = "Lecture Notes in Computer Science",
	edition = "",
	pages = "12-12",
	publisher = "Springer",
	address = "Cham",
	url = "https://link.springer.com/chapter/10.1007/978-3-319-09761-9_2"
}

TY  - CHAP
TI  - Improving speech recognition through automatic selection of age group – specific acoustic models
T2  - Computational Processing of the Portuguese Language. PROPOR 2014. Lecture Notes in Computer Science
VL  - 8775
AU  - Hämäläinen, A.
AU  - Meinedo, H.
AU  - Tjalve, M.
AU  - Pellegrini, T.
AU  - Trancoso, I.
AU  - Dias, M. S.
PY  - 2014
SP  - 12-23
SN  - 0302-9743
DO  - 10.1007/978-3-319-09761-9_2
CY  - Cham
UR  - https://link.springer.com/chapter/10.1007/978-3-319-09761-9_2
AB  - The acoustic models used by automatic speech recognisers are usually trained with speech collected from young to middle-aged adults. As the characteristics of speech change with age, such acoustic models tend to perform poorly on children’s and elderly people’s speech. In this study, we investigate whether the automatic age group classification of speakers, together with age group –specific acoustic models, could improve automatic speech recognition performance. We train an age group classifier with an accuracy of about 95% and show that using the results of the classifier to select age group –specific acoustic models for children and the elderly leads to considerable gains in automatic speech recognition performance, as compared with using acoustic models trained with young to middle-aged adults’ speech for recognising their speech, as well.



    

ER  -