1. ACOUSTIC ANALYSIS Acoustic methods applied to language
Three main tools
Praat¶
MATLAB
Winpitch: F0, pitch tracking, pitch detection algorithms, on the fly alignment

2. 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

3. the kernel density estimate method
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 3

4. Outline 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

5. Maptask from Anderson et al. 1991

6. 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)

7. Méli (PhD in progress)

8. DATA
20 female speakers / 5 male speakers
Syllable features
CVC / CV :

9. Speaker 20 Session 2: A preliminary analysis (Méli, in progress)

10. Unnormalized per-monopthong F1 and F2 values
each dot represents the occurrence of one monophthong

12. Duration values for each monophthong (in seconds)

13. Duration for diphthongs

16. Per-diphthong distribution of the vectorial coefficients

17. DATA (duration of recordings)

18. 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
PAIRWISE DIFFERENCES for time series
CT ree
ctree() C5.0 package
rattle

19. Kernels (Paroissin 2015)

20. Density estimates
Don’t use with this kind of data:

21. NO ASSUMPTIONS: PLOTTING THE DATA
continuous functions :
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

22. Kde

23. 3D representations of kde

24. Distribution per word

25. R packages NPDEN TESTING THE RELEVANCE NPUDIST Favorite mgcv PACKAGE
library(splines)

26. DURATION > 0.03 s (aligners…)

27. Kde unidimensional density
Bimodal distributions of duration ?

28. F2 x duration

30. Kernel effects (?)

31. Similar

32. Kitchen sink methods All learners All dimensions
Pairwise comparisons for sessions

33. 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

34. REFERENCES
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  Analyzing linguistic data (Vol. 505). Cambridge, UK: Cambridge University Press.Best, C. T 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 Frequency of Use and the Organization of Language. Oxford: Oxford University Press. Bybee, J Language, Usage and Cognition. Cambridge: Cambridge University Press
Boersma, P. & Weenink, D. (2005). Praat: doing phonetics by computer (Version ). Retrieved from 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), R Development Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN , URL Gramacki, A., & Gramacki, J. (2015). FFT-Based Fast Computation of Multivariate Kernel Estimators with Unconstrained Bandwidth Matrices. arXiv preprint arXiv: Wand, M. and B. Ripley (2015). Functions for Kernel Smoothing Supporting Wand & Jones (1995). R package version Wand, M. and M. Jones (1995). Kernel Smoothing. Chapman & Hall.

35. Some references
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,
Díaz Negrillo, A A Fine-Grained Error Tagger for Learner Corpora. Ph.D. thesis, University of Jaén, Spain.
Díaz-Negrillo, A., Ballier, N., & Thompson, P. (Eds.)  Automatic Treatment and Analysis of Learner Corpus Data (Vol. 59). John Benjamins Publishing Company.
Granger et. al., Sylviane “The LONGDALE Project Longitudinal Database of Learner English”.
Gries, S. [2009] 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 (10/09)
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 ANGLISH. (10/09)
PRAAT : praat.org
R :
SPPAS :
WinPitch: winpitch.com

36. Gries, S. [2009] 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 (10/09)

37. THANKS ! 37

38. ALTERNATES

39. 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)