Linguistic pitch analysis using functional principal component mixed effect models
AbstractFundamental frequency (F0, broadly 'pitch') is an integral part of spoken human language; however, a comprehensive quantitative model for F0 can be a challenge to formulate owing to the large number of effects and interactions between effects that lie behind the human voice's production of F0, and the very nature of the data being a contour rather than a point. The paper presents a semiparametric functional response model for F0 by incorporating linear mixed effects models through the functional principal component scores. This model is applied to the problem of modelling F0 in the tone language Qiang, a language in which relative pitch information is part of each word's dictionary entry. Copyright (c) 2010 Royal Statistical Society.
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Bibliographic InfoArticle provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series C (Applied Statistics).
Volume (Year): 59 (2010)
Issue (Month): 2 ()
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- Han Lin Shang, 2011.
"A survey of functional principal component analysis,"
Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics
6/11, Monash University, Department of Econometrics and Business Statistics.
- Han Shang, 2014. "A survey of functional principal component analysis," AStA Advances in Statistical Analysis, Springer, Springer, vol. 98(2), pages 121-142, April.
- Shang, Han Lin, 2013. "Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 67(C), pages 185-198.
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