Publication

Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English

Wieling, M., Sep-2018, In : Journal of Phonetics. 70, p. 86-116 31 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Wieling, M. (2018). Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English. Journal of Phonetics, 70, 86-116. https://doi.org/10.1016/j.wocn.2018.03.002

Author

Wieling, Martijn. / Analyzing dynamic phonetic data using generalized additive mixed modeling : a tutorial focusing on articulatory differences between L1 and L2 speakers of English. In: Journal of Phonetics. 2018 ; Vol. 70. pp. 86-116.

Harvard

Wieling, M 2018, 'Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English', Journal of Phonetics, vol. 70, pp. 86-116. https://doi.org/10.1016/j.wocn.2018.03.002

Standard

Analyzing dynamic phonetic data using generalized additive mixed modeling : a tutorial focusing on articulatory differences between L1 and L2 speakers of English. / Wieling, Martijn.

In: Journal of Phonetics, Vol. 70, 09.2018, p. 86-116.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Wieling M. Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English. Journal of Phonetics. 2018 Sep;70:86-116. https://doi.org/10.1016/j.wocn.2018.03.002


BibTeX

@article{b7c4208dc2c044e389817f1503c989ee,
title = "Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English",
abstract = "In phonetics, many datasets are encountered which deal with dynamic data collected over time. Examples include diphthongal formant trajectories and articulator trajectories observed using electromagnetic articulography. Traditional approaches for analyzing this type of data generally aggregate data over a certain timespan, or only include measurements at a fixed time point (e.g., formant measurements at the midpoint of a vowel). This paper discusses generalized additive modeling, a non-linear regression method which does not require aggregation or the pre-selection of a fixed time point. Instead, the method is able to identify general patterns over dynamically varying data, while simultaneously accounting for subject and item-related variability. An advantage of this approach is that patterns may be discovered which are hidden when data is aggregated or when a single time point is selected. A corresponding disadvantage is that these analyses are generally more time consuming and complex. This tutorial aims to overcome this disadvantage by providing a hands-on introduction to generalized additive modeling using articulatory trajectories from L1 and L2 speakers of English within the freely available R environment. All data and R code is made available to reproduce the analysis presented in this paper.",
keywords = "PERCEPTION, SPEECH",
author = "Martijn Wieling",
year = "2018",
month = "9",
doi = "10.1016/j.wocn.2018.03.002",
language = "English",
volume = "70",
pages = "86--116",
journal = "Journal of Phonetics",
issn = "0095-4470",
publisher = "ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD",

}

RIS

TY - JOUR

T1 - Analyzing dynamic phonetic data using generalized additive mixed modeling

T2 - a tutorial focusing on articulatory differences between L1 and L2 speakers of English

AU - Wieling, Martijn

PY - 2018/9

Y1 - 2018/9

N2 - In phonetics, many datasets are encountered which deal with dynamic data collected over time. Examples include diphthongal formant trajectories and articulator trajectories observed using electromagnetic articulography. Traditional approaches for analyzing this type of data generally aggregate data over a certain timespan, or only include measurements at a fixed time point (e.g., formant measurements at the midpoint of a vowel). This paper discusses generalized additive modeling, a non-linear regression method which does not require aggregation or the pre-selection of a fixed time point. Instead, the method is able to identify general patterns over dynamically varying data, while simultaneously accounting for subject and item-related variability. An advantage of this approach is that patterns may be discovered which are hidden when data is aggregated or when a single time point is selected. A corresponding disadvantage is that these analyses are generally more time consuming and complex. This tutorial aims to overcome this disadvantage by providing a hands-on introduction to generalized additive modeling using articulatory trajectories from L1 and L2 speakers of English within the freely available R environment. All data and R code is made available to reproduce the analysis presented in this paper.

AB - In phonetics, many datasets are encountered which deal with dynamic data collected over time. Examples include diphthongal formant trajectories and articulator trajectories observed using electromagnetic articulography. Traditional approaches for analyzing this type of data generally aggregate data over a certain timespan, or only include measurements at a fixed time point (e.g., formant measurements at the midpoint of a vowel). This paper discusses generalized additive modeling, a non-linear regression method which does not require aggregation or the pre-selection of a fixed time point. Instead, the method is able to identify general patterns over dynamically varying data, while simultaneously accounting for subject and item-related variability. An advantage of this approach is that patterns may be discovered which are hidden when data is aggregated or when a single time point is selected. A corresponding disadvantage is that these analyses are generally more time consuming and complex. This tutorial aims to overcome this disadvantage by providing a hands-on introduction to generalized additive modeling using articulatory trajectories from L1 and L2 speakers of English within the freely available R environment. All data and R code is made available to reproduce the analysis presented in this paper.

KW - PERCEPTION

KW - SPEECH

UR - http://martijnwieling.nl/files/GAM-tutorial-Wieling.pdf

U2 - 10.1016/j.wocn.2018.03.002

DO - 10.1016/j.wocn.2018.03.002

M3 - Article

VL - 70

SP - 86

EP - 116

JO - Journal of Phonetics

JF - Journal of Phonetics

SN - 0095-4470

ER -

ID: 56590919