ACOUSTIC ANALYSIS
• Acoustic methods applied to language
Three main tools
• Praat¶
• MATLAB
• Winpitch: F0, pitch tracking, pitch detection
algorithms, on the fly alignment
Some PhDs on phonetics
data collection / empirical
- Adrien Méli: PhD on French pronunciation of vowels
- Maelle Amand: diphthongs in NECTE (co-supervision with Karen
Corrigan and Aurélie Fischer) : analysing a Research project from
the 1960’s with current data mining techniques :
- -Corpus analysis (vowel) Identification of variation / classification
tasks
- Aurélie Chlébowski : Acoustic analysis of nasal grunts
-Esther Legrézause analysis of UM/HUM
+ misc. Analyses on data mining approaches, MA on accommodation:
how you imitate natives as a learner (repetition task): Léa Burin
Analysing the emergence of vowel
categorisation in a longitudinal learner
corpus:
the kernel density estimate method
Caen EPIP5
17 May 2017
Nicolas Ballier & Adrien Méli
UFR études anglophones
EA 3967, CLILLAC-ARP
Univ Paris Diderot Sorbonne Paris Cité
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Outline
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Research questions
Corpus
A Non-parametric approach: density estimation
Unidimensional approach: Non-native and
duration as cue to KIT/FLEECE
• Multidimensional approach: 3D representations
of F1, F2 and duration
• The ‘window’ effect : the kernel method
Maptask from Anderson et al. 1991
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Protocols and RQs
• Phonological awareness:
• Emphasis and read speech : ability to render
italics (the ‘Landlady’)
• Stress patterns (Cameron’s speech)
Missing:
• Perception tasks…
• Repetition tasks (ANGLISH)
• Reformulation tasks (LeaP)
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Méli (PhD in progress)
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DATA
• 20 female speakers / 5 male speakers
• Syllable features
• CVC / CV :
Speaker 20 Session 2:
A preliminary analysis (Méli, in progress)
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Unnormalized per-monopthong F1 and
F2 values
each dot represents the occurrence of one monophthong
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Duration values for each
monophthong (in seconds)
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Duration for diphthongs
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Per-diphthong distribution of the
vectorial coefficients
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DATA (duration of recordings)
MODELS
• Cosine transforms
- filtering DATA
• lm models and
• repeated measures : mi
• random //
• word: fixed effects
FREQUENCY OF LEARNERS: + FREQUENCY OF
logistic regression ->
• parametric analysis
Central limit theory >> to too many outliers.
summarize the word: AVERAGE of
• word position
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PAIRWISE DIFFERENCES for time series
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CT ree
ctree() C5.0 package
rattle
Kernels (Paroissin 2015)
Density estimates
• Don’t use with this kind of data:
NO ASSUMPTIONS: PLOTTING THE
DATA
• continuous functions :
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assumptions about the data :
no discontinus DTAA
- cross validation for the better
- variance / bias (biased:one number // )
density estimates ONE D
- bandwidth is a box: cross validation in boxes
• - bandwidth
• -> SAILING AND NORMALIZING THE DATA : means(=variance 1) SD divide
by
Kde
3D representations of kde
Distribution per word
R packages
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NPDEN
TESTING THE RELEVANCE
NPUDIST
Favorite mgcv PACKAGE
library(splines)
DURATION > 0.03 s (aligners…)
Kde unidimensional density
• Bimodal distributions of duration ?
F2 x duration
Kernel effects (?)
Similar
Kitchen sink methods
• All learners
• All dimensions
• Pairwise comparisons for sessions
NEXT PLANS
• PhD viva
• R package with most of the coding
• Paper describing the syllable algorithm >
Github
• Data paper with some subsamples of the data
REFERENCES
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Ballier, N., & Martin, P. (2013). Developing corpus interoperability for phonetic investigation of
learner corpora. Automatic Treatment and Analysis of Learner Corpus Data, 59.
Baayen, R. H. 2008. Analyzing linguistic data (Vol. 505). Cambridge, UK: Cambridge University
Press.Best, C. T. 1995. A direct realist view of cross-language speech perception. In: Strange, W.,
(ed),Speech perception and linguistic experience: Theoretical and methodological issues. Baltimore:
York Press, 171– 204.
Bybee, J. 2007.Frequency of Use and the Organization of Language. Oxford: Oxford University
Press. Bybee, J. 2010.Language, Usage and Cognition. Cambridge: Cambridge University Press
Boersma, P. & Weenink, D. (2005). Praat: doing phonetics by computer (Version 5.3.71).
Retrieved from http://www.praat.org.
Bigi, B. (2012). Sppas: A tool for the phonetic segmentations of speech. In Proceedings of LREC
2012, pp. 1748–1755.
De Cara B, & Goswami U. (2002). Similarity relations among spoken words: The special status of
rimes in English. Behavior Research Methods, Instruments, & Computers, 34 (3), 416-423
R Development Core Team (2013). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-90005107-0, URL http://www.R-project.org
Gramacki, A., & Gramacki, J. (2015). FFT-Based Fast Computation of Multivariate Kernel Estimators
with Unconstrained Bandwidth Matrices. arXiv preprint arXiv:1508.02766.
http://arxiv.org/pdf/1508.02766.pdf
Wand, M. and B. Ripley (2015). Functions for Kernel Smoothing Supporting Wand & Jones (1995). R
package version 2.23-15.
Wand, M. and M. Jones (1995). Kernel Smoothing. Chapman & Hall.
Some references
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Cauvin, E, 2013, « Intonational phrasing as a potential indicator for establishing prosodic learner profiles ». In S. Granger, G.
Gilquin & F. Meunier (eds) (2013) Twenty Years of Learner Corpus Research: Looking back, Moving ahead. Corpora and
Language in Use - Proceedings 1, Louvain-la-Neuve: Presses universitaires de Louvain, 75-88.
Díaz Negrillo, A. 2007. A Fine-Grained Error Tagger for Learner Corpora. Ph.D. thesis, University of Jaén, Spain.
Díaz-Negrillo, A., Ballier, N., & Thompson, P. (Eds.). 2013. Automatic Treatment and Analysis of Learner Corpus Data (Vol. 59).
John Benjamins Publishing Company.
Granger et. al., Sylviane. 2009. “The LONGDALE Project Longitudinal Database of Learner English”.
http://cecl.fltr.ucl.ac.be/LONGDALE.html
Gries, S. [2009] 2013. Statistics for Linguists: A Practical Introduction. Berlin, New York: Mouton de Gruyter
Herment, S., Loukina, A., Tortel, A., Hirst, D., Bigi, B., 2013, AixOx, a multi-layered learners corpus: automatic annotation. In
Díaz Pérez Javier & Díaz Negrillo Ana (eds.). Specialisation and variation in language corpora. Bern : Peter Lang.
Méli, A., 2010, Aspects of a longitudinal corpus-based study of French learners of English. A preliminary investigation,
Mémoire de Master 2 non-publié, sous la direction de N. Ballier, Université Paris Diderot.
Méli, A. 2013, Phonological acquisition in the French-English interlanguage. Rising above the phoneme in Díaz-Negrillo, A.,
N. Ballier and P. Thompson (eds.), Automatic Treatment and Analysis of Learner Corpus Data, Amsterdam :Benjamins, 207–
226.
Milde, Jan-Torsten. & Gut, Ulrike (2002). “A Prosodic Corpus of Non-native Speech”. Speech prosody 2002. (10/09)
http://aune.lpl.univ-aix.fr/sp2002/pdf/milde-gut.pdf
Myssyk, A, 2011 Predicting and evaluating a speaker's level of English : a proposal for pronunciation criteria , Mémoire de
Master 2 non-publié, sous la direction de N. Ballier, Université Paris Diderot.
Tortel, A. & Hirst, D. 2008. ANGLISH. (10/09) http://crdo.fr/crdo000731
PRAAT : praat.org
R : http://www.r-project.org/
SPPAS : http://aune.lpl.univ-aix.fr/~bigi/sppas/
WinPitch: winpitch.com
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Gries, S. [2009] 2013. Statistics for Linguists: A Practical Introduction. Berlin, New
York: Mouton de Gruyter
Herment, S., Loukina, A., Tortel, A., Hirst, D., Bigi, B., 2013, AixOx, a multi-layered
learners corpus: automatic annotation. In Díaz Pérez Javier & Díaz Negrillo Ana
(eds.). Specialisation and variation in language corpora. Bern : Peter Lang.
Méli, A., 2010, Aspects of a longitudinal corpus-based study of French learners of
English. A preliminary investigation, Mémoire de Master 2 non-publié, sous la
direction de N. Ballier, Université Paris Diderot.
Méli, A. 2013, Phonological acquisition in the French-English interlanguage. Rising
above the phoneme in Díaz-Negrillo, A., N. Ballier and P. Thompson (eds.),
Automatic Treatment and Analysis of Learner Corpus Data, Amsterdam :Benjamins,
207–226.
Milde, Jan-Torsten. & Gut, Ulrike (2002). “A Prosodic Corpus of Non-native
Speech”. Speech prosody 2002. (10/09) http://aune.lpl.univaix.fr/sp2002/pdf/milde-gut.pdf
http://bookzz.org/dl/2298946/b555a2
THANKS !
[email protected]
[email protected]
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ALTERNATES
Some Perspectives after Adrien Méli
PhD
• Correlation with usage frequency ?
• Attractors (« lexical » magnets, modelling
realisations for lexical sets (people) ?
• « Templatic effects » transfers of French
syllable structures (CVC vs. CV)
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