scholarly journals Tracking the New Zealand English NEAR/SQUARE Merger Using Functional Principal Components Analysis

2019 ◽  
Author(s):  
Michele Gubian ◽  
Jonathan Harrington ◽  
Mary Stevens ◽  
Florian Schiel ◽  
Paul Warren
Keyword(s):  
New Zealand ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis ◽  
Functional Principal Components Analysis ◽  
Functional Principal Components

scholarly journals Tracking the New Zealand English NEAR/SQUARE merger using functional principal components analysis

2020 ◽  
Author(s):  
M Gubian ◽  
J Harrington ◽  
M Stevens ◽  
F Schiel ◽  
Paul Warren
Keyword(s):  
New Zealand ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Age Groups ◽  
Low Frequency ◽  
Sound Change ◽  
Formant Frequencies ◽  
Components Analysis ◽  
Functional Principal Components Analysis ◽  
Functional Principal Components

Copyright © 2019 ISCA The focus of the study is the application of functional principal components analysis (FPCA) to a sound change in progress in which the SQUARE and NEAR falling diphthongs are merging in New Zealand English. FPCA approximated the trajectory shapes of the first two formant frequencies (F1/F2) in a large acoustic database of read New Zealand English speech spanning three different age groups and two regions. The derived FPCA parameters showed a greater degree of centralisation and monophthongisation in SQUARE than in NEAR. Compatibly with the evidence of an ongoing sound change in which SQUARE is shifting towards NEAR, these shape differences were more marked for older than for younger/mid-age speakers. There was no effect of region nor of place of articulation of the preceding consonant; there was a trend for the merger to be more advanced in low frequency words. The study underlines the benefits of FPCA for quantifying the many types of sound changes involving subtle shifts in speech dynamics. In particular, multi-dimensional trajectory shape differences can be quantified without the need for vowel targets nor for determining the influence of the parameters - in this case of the first two formant frequencies - independently of each other.

scholarly journals Tracking the New Zealand English NEAR/SQUARE merger using functional principal components analysis

2020 ◽  
Author(s):  
M Gubian ◽  
J Harrington ◽  
M Stevens ◽  
F Schiel ◽  
Paul Warren
Keyword(s):  
New Zealand ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Age Groups ◽  
Low Frequency ◽  
Sound Change ◽  
Formant Frequencies ◽  
Components Analysis ◽  
Functional Principal Components Analysis ◽  
Functional Principal Components

Copyright © 2019 ISCA The focus of the study is the application of functional principal components analysis (FPCA) to a sound change in progress in which the SQUARE and NEAR falling diphthongs are merging in New Zealand English. FPCA approximated the trajectory shapes of the first two formant frequencies (F1/F2) in a large acoustic database of read New Zealand English speech spanning three different age groups and two regions. The derived FPCA parameters showed a greater degree of centralisation and monophthongisation in SQUARE than in NEAR. Compatibly with the evidence of an ongoing sound change in which SQUARE is shifting towards NEAR, these shape differences were more marked for older than for younger/mid-age speakers. There was no effect of region nor of place of articulation of the preceding consonant; there was a trend for the merger to be more advanced in low frequency words. The study underlines the benefits of FPCA for quantifying the many types of sound changes involving subtle shifts in speech dynamics. In particular, multi-dimensional trajectory shape differences can be quantified without the need for vowel targets nor for determining the influence of the parameters - in this case of the first two formant frequencies - independently of each other.

scholarly journals Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis

2018 ◽  
Vol 113 (523) ◽  
pp. 1003-1015 ◽  
Author(s):  
Daniel Backenroth ◽  
Jeff Goldsmith ◽  
Michelle D. Harran ◽  
Juan C. Cortes ◽  
John W. Krakauer ◽  
...  
Keyword(s):  
Motor Learning ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis ◽  
Functional Principal Components Analysis ◽  
Functional Principal Components
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