Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

Journal of the American Statistical Association

Volumn 110, Issue 510, 2015, Pages 545-559

  Hadjipantelis, P Z ^a   Aston, J A D ^a,b   Muller H G ^c   Evans, J P ^d  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d INSTITUTE OF LINGUISTICS   (Taiwan)

Author keywords

Functional data analysis; Linguistics; Multivariate linear mixed models; Phonetic analysis; Registration

Indexed keywords

EID: 84936773585     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2015.1006729     Document Type: Article

References (76)

1. J.Aitchison, (1982), “The Statistical Analysis of Compositional Data,” Journal of the Royal Statistical Society, Series B, 44, 139–177.
2. ——— (1983), “Principal Component Analysis of Compositional Data,” Biometrika, 70, 57–65.
3. J.Aitchison,, M.Greenacre, (2002), “Biplots of Compositional Data,” Journal of the Royal Statistical Society, Series C, 51, 375–392.
4. J.Aston,, J.Chiou,, J.Evans, (2010), “Linguistic Pitch Analysis using Functional Principal Component Mixed Effect Models,” Journal of the Royal Statistical Society, Series C, 59, 297–317.
5. R.Baayen,, D.Davidson,, D.Bates, (2008), “Mixed-Effects Modeling With Crossed Random Effects for Subjects and Items,” Journal of Memory and Language, 59, 390–412.
6. R.H.Baayen, (2008), Analyzing Linguistic Data. A Practical Introduction to Statistics using R, Cambridge, UK: Cambridge University Press.
7. D.J.Barr,, R.Levy,, C.Scheepers,, H.J.Tily, (2013), “Random Effects Structure for Confirmatory Hypothesis Testing: Keep it Maximal,” Journal of Memory and Language, 68, 255–278.
8. D.Bates,, M.Maechler,, B.Bolker,, and S.Walker, (2013), lme4: Linear Mixed-Effects Models Using Eigen and S4, r package version 1.0-4, Vienna, Austria: R Foundation for Statistical Computing.
9. Beckman, M. E., Hirschberg, J., and Shattuck-Hufnagel, S. (2006), “The Original ToBI system and the Evolution of the ToBI Framework,” in Prosodic Typology: The Phonology of Intonation and Phrasing, ed. S.-A. Jun, pp. 9–54. Oxford: Oxford University Press.
10. J.Benesty,, M.M.Sondhi,, and Y.Huang, (2008), Springer Handbook of Speech Processing, Berlin Heidelberg, Germany: Springer, Chapters 21, p. 445; 23, pp. 481–484; 36, pp. 731–732.
11. M.Benko,, W.Härdle,, A.Kneip, (2009), “Common Functional Principal Components,” The Annals of Statistics, 37, 1–34.
12. A.W.Black,, A.Hunt, (1996), “Generating F0 Contours From to BI Labels Using Linear Regression,” in ICSLP, pp. 1385–1388.
13. B.A.Brumback,, J.A.Rice, (1998), “Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves,” Journal of the American Statistical Association, 93, 961–976.
14. A.Bruns, (2004), “Fourier-, Hilbert- and Wavelet-Based Signal Analysis: Are They Really Different Approaches? Journal of Neuroscience Methods, 137, 321–332.
15. P.Buser,, and M.Imbert, (1992), Audition (1st ed.), Cambridge, MA: MIT Press, Chapter 2.
16. R.Cangelosi,, and A.Goriely, (2007), “Component Retention in Principal Component Analysis With Application to cDNA Microarray Data,” Biology Direct, 2, 2+.
17. P.Castro,, W.Lawton,, E.Sylvestre, (1986), “Principal Modes of Variation for Processes With Continuous Sample Curves,” Technometrics, 28, 329–337.
18. Central Intelligence Agency (2012), “The CIA World Factbook,” available at https://www.cia.gov/library/publications/the-world-factbook/geos/xx.html.
19. K.Chen,, and H.-G.Müller, (2014), “Modeling Conditional Distributions for Functional Responses, With Application to Traffic Monitoring via GPS-Enabled Mobile Phones,” Technometrics, 56, 347–358.
20. J.Chiou,, H.Müller,, J.Wang, (2003), “Functional Quasi-Likelihood Regression Models With Smooth Random Effects,” Journal of the Royal Statistical Society, Series B, 65, 405–423.
21. J.J.Egozcue,, V.Pawlowsky-Glahn,, G.Mateu-Figueras,, C.Barceló-Vidal, (2003), “Isometric Logratio Transformations for Compositional Data Analysis,” Mathematical Geology, 35, 279–300.
22. J.Evans,, M.Chu,, J.Aston,, C.Su, (2010), “Linguistic and Human Effects on F0 in a Tonal Dialect of Qiang,” Phonetica, 67, 82–99.
23. P.Filzmoser,, K.Hron,, C.Reimann, (2009), “Principal Component Analysis for Compositional Data With Outliers,” Environmetrics, 20, 621–632.
24. B.N.Flury, (1984), “Common Principal Components in k Groups,” Journal of the American Statistical Association, 79, 892–898.
25. H.Fujisaki, (2004), “Information, Prosody, and Modeling-With Emphasis on Tonal Features of Speech,” in Speech Prosody 2004, International Conference, ISCA, pp. 1–10.
26. E.Grabe,, G.Kochanski,, J.Coleman, (2007), “Connecting Intonation Labels to Mathematical Descriptions of Fundamental Frequency,” Language and Speech, 50, 281–310.
27. S.Greven,, T.Kneib, (2010), “On the Behaviour of Marginal and Conditional AIC in Linear Mixed Models,” Biometrika, 97, 773–789.
28. W.Gu,, K.Hirose,, H.Fujisaki, (2006), “Modeling the Effects of Emphasis and Question on Fundamental Frequency Contours of Cantonese Utterances,” IEEE Transactions on Audio, Speech, and Language Processing, 14, 1155–1170.
29. W.Guo, (2002), “Functional Mixed Effects Models,” Biometrics, 58, 121–128.
30. J.D.Hadfield, (2010), “MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package,” Journal of Statistical Software, 33, 1–22.
31. P.Hadjipantelis,, J.Aston,, J.Evans, (2012), “Characterizing Fundamental Frequency in Mandarin: A Functional Principal Component Approach Utilizing Mixed Effect Models,” Journal of the Acoustical Society of America, 131, 4651–4664.
32. D.Hirst,, R.Espesser, (1993), “Automatic Modelling of Fundamental Frequency Using a Quadratic Spline Function,” Travaux de l’Institut de phonétique d’Aix, 15, 71–85.
33. A.Hyvärinen,, E.Oja, (2000), “Independent Component Analysis: Algorithms and Applications,” Neural Networks, 13, 411–430.
34. A.Izenman, (2008), Modern Multivariate Statistical Techniques: Regression, Classification and Manifold Learning, New York: Springer Verlag, Chapter 6.
35. E.Jacewicz,, R.A.Fox,, L.Wei, (2010), “Between-Speaker and Within-Speaker Variation in Speech Tempo of American English,” Journal of the Acoustical Society of America, 128, 839–850.
36. D.Jurafsky,, and J.H.Martin, (2009), Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd ed.), Upper Saddle River, NJ: Prentice Hall PTR, Chapter 7.3.
37. C.T.Kelley, (1999), “Iterative Methods for Optimization,” in Frontiers in Applied Mathematics, Philadelphia, PA: SIAM.
38. A.Kneip,, T.Gasser, (1992), “Statistical Tools to Analyze Data Representing a Sample of Curves,” Annals of Statistics, 20, 1266–1305.
39. A.Kneip,, J.Ramsay, (2008), “Combining Registration and Fitting for Functional Model,” Journal of the American Statistical Association, 103, 1155–1165.
40. G.Kochanski, (2006), “Prosody Beyond Fundamental Frequency,” in Methods in Empirical Prosody Research, ed. S. Sudhoff, Berlin: Walter De Gruyter Inc, pp. 89–123.
41. L.L.Koening,, J.C.Lucero,, E.Perlman, (2008), “Speech Production Variability in Fricatives of Children and Adults: Results of Functional Data Analysis,” Journal of the Acoustical Society of America, 5, 3158–3170.
42. S.Kurtek,, A.Srivastava,, E.Klassen,, Z.Ding, (2012), “Statistical Modeling of Curves Using Shapes and Related Features,” Journal of the American Statistical Association, 107, 1152–1165.
43. N.M.Laird,, J.H.Ware, (1982), “Random-Effects Models for Longitudinal Data,” Biometrics, 38, 963–974.
44. V.Latsch,, and S.L.Netto, (2011), “Pitch-Synchronous Time Alignment of Speech Signals for Prosody Transplantation,” in International Symposium on Circuits and Systems (ISCAS 2011), IEEE, pp. 2405–2408.
45. S.Lee,, D.Byrd,, J.Krivokapic, (2006), “Functional Data Analysis of Prosodic Effects on Articulatory Timing,” Journal of the Acoustical Society of America, 119, 1666–1671.
46. T.Leonard, (1973), “A Bayesian Method for Histograms,” Biometrika, 60, 297–308.
47. M.A.Lindquist,, J.Spicer,, I.Asllani,, T.D.Wager, (2012), “Estimating and Testing Variance Components in a Multi-Level GLM,” Neuroimage, 59, 490–501.
48. X.Liu,, H.G.Müller, (2004), “Functional Convex Averaging and Synchronization for Time-Warped Random Curves,” Journal of the American Statistical Association, 99, 687–699.
49. J.Louw,, E.Barnard, (2004), “Automatic Intonation Modeling With INTSINT,” in Proceedings of the Pattern Recognition Association of South Africa, pp. 107–111.
50. J.Mercer, (1909), “Functions of Positive and Negative Type, and Their Connection With the Theory of Integral Equations,” Philosophical Transactions of the Royal Society of London, Series A, 209, 415–446.
51. T.P.Minka, (2001), “Automatic Choice of Dimensionality for PCA,” Advances in Neural Information Processing Systems, 15, 598–604.
52. H.Mixdorff, (2000), “A Novel Approach to the Fully Automatic Extraction of Fujisaki Model Parameters,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2000. ICASSP’00, IEEE (Vol. 110), pp. 1281–1284.
53. H.D.Patterson,, R.Thomson, (1971), “Recovery of Inter-Block Information When Block Sizes are Unequal,” Biometrika, 58, 545–554.
54. V.Pawlowsky-Glahn,, J.Egozcue, (2006), “Compositional Data and Their Analysis: An Introduction,” Geological Society, London, Special Publications, 264, 1–10.
55. J.Pinheiro,, D.Bates,, S.DebRoy,, D.Sarkar,, and R Core Team (2013), nlme: Linear and Nonlinear Mixed Effects Models, r package version 3.1-109, Vienna, Austria: R Foundation for Statistical Computing.
56. S.Prom-On,, Y.Xu,, B.Thipakorn, (2009), “Modeling Tone and Intonation in Mandarin and English as a Process of Target Approximation,” The Journal of the Acoustical Society of America, 125, 405–424.
57. H.Quene, (2007), “On the Just Noticeable Difference for Tempo in Speech,” Journal of Phonetics, 35, 353–362.
58. L.Rabiner, (1989), “A Tutorial on HMM and Selected Applications in Speech Recognition,” Proceedings of the IEEE, 77, 257–286.
59. J.Ramsay,, X.Li, (1998), “Curve Registration,” Journal of the Royal Statistical Society, Series B, 60, 351–363.
60. J.Ramsay,, and B.Silverman, (2005), Functional Data Analysis, New York: Springer Verlag, Chapters 1, pp. 9–15; 3, pp. 38–45; 8, pp. 148–154.
61. J.O.Ramsay,, K.Munhall,, V.Gracco,, D.Ostry, (1996), “Functional Data Analyses of Lip Motion,” Journal of the Acoustical Society of America, 99, 3718–3727.
62. H.Sakoe, (1979), “Two-Level DP-Matching—A Dynamic Programming-Based Pattern Matching Algorithm for Connected Word Recognition,” IEEE Transactions on Acoustics, Speech and Signal Processing, 27, 588–595.
63. H.Schielzeth,, W.Forstmeier, (2009), “Conclusions Beyond Support: Overconfident Estimates in Mixed Models,” Behavioral Ecology, 20, 416–420.
64. S.R.Searle,, G.Casella,, and C.E.McCulloch, (2006), Variance Components (2nd ed.), Hoboken, NJ: Wiley.
65. Z.Su,, and Z.Wang, (2005), “An Approach to Affective-Tone Modeling for Mandarin,” in Affective Computing and Intelligent Interaction Lecture Notes in Computer Science, Vol. 3784, eds. J. Tao, T.Tan, and R. Picard, BerlinHeidelberg: Springer, pp. 390–396.
66. P.Tang,, H.Müller, (2008), “Pairwise Curve Synchronization for High-Dimensional Data,” Biometrika, 95, 875–889.
67. ——— (2009), “Time-Synchronized Clustering of Gene Expression Trajectories,” Biostatistics, 10, 32–45.
68. P.Taylor, (2000), “Analysis and Synthesis of Intonation Using the Tilt Model,” The Journal of the Acoustical Society of America, 107, 1697–1714.
69. R.C.Torgerson, (2005), “A Comparison of Beijing and Taiwan Mandarin Tone Register: An Acoustic Analysis of Three Native Speech Styles,” M.Sc. dissertation. Brigham Young University, Provo, UT.
70. C.Tseng,, Y.Cheng,, C.Chang, (2005), “Sinica COSPRO and Toolkit: Corpora and Platform of Mandarin Chinese Fluent Speech,” in Proceedings of Oriental COCOSDA, pp. 6–8.
71. J.D.Tucker,, W.Wu,, A.Srivastava, (2013), “Generative Models for Functional Data Using Phase and Amplitude Separation,” Computational Statistics & Data Analysis, 61, 50–66.
72. G.N.Wilkinson, C.E.Rogers, (1973), “Symbolic Description of Factorial Models for Analysis of Variance,” Journal of the Royal Statistical Society, Series C, 22, 392–399.
73. Y.Xu, (1999), “Effects of Tone and Focus on the Formation and Alignment of f0 Contours,” Journal of Phonetics, 27, 55–105.
74. Y.Xu,, Q.E.Wang, (2001), “Pitch Targets and Their Realization: Evidence From Mandarin Chinese,” Speech Communication, 33, 319–337.
75. T.Yoshioka,, A.Sehr,, M.Delcroix,, K.Kinoshita,, R.Maas,, T.Nakatani,, W.Kellermann, (2012), “Making Machines Understand us in Reverberant Rooms: Robustness Against Reverberation for Automatic Speech Recognition,” IEEE Signal Processing Magazine, 29, 114–126.
76. Z.Zhang,, H.-G.Müller, (2011), “Functional Density Synchronization,” Computational Statistics and Data Analysis, 55, 2234–2249.