21
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Analyzing speech in both time and space: Generalized additive mixed models can uncover systematic patterns of variation in vocal tract shape in real-time MRI

      research-article

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We present a method of using generalized additive mixed models (GAMMs) to analyze midsagittal vocal tract data obtained from real-time magnetic resonance imaging (rt-MRI) video of speech production. Applied to rt-MRI data, GAMMs allow for observation of factor effects on vocal tract shape throughout two key dimensions: time (vocal tract change over the temporal course of a speech segment) and space (location of change within the vocal tract). Examples of this method are provided for rt-MRI data collected at a temporal resolution of 20 ms and a spatial resolution of 1.41 mm, for 36 native speakers of German. The rt-MRI data were quantified as 28-point semi-polar-grid aperture functions. Three test cases are provided as a way of observing vocal tract differences between: (1) /aː/ and /iː/, (2) /aː/ and /aɪ/, and (3) accentuated and unstressed /aː/. The results for each GAMM are independently validated using functional linear mixed models (FLMMs) constructed from data obtained at 20% and 80% of the vowel interval. In each case, the two methods yield similar results. In light of the method similarities, we propose that GAMMs are a robust, powerful, and interpretable method of simultaneously analyzing both temporal and spatial effects in rt-MRI video of speech.

          Related collections

          Most cited references56

          • Record: found
          • Abstract: not found
          • Article: not found

          Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Low-rank scale-invariant tensor product smooths for generalized additive mixed models.

            A general method for constructing low-rank tensor product smooths for use as components of generalized additive models or generalized additive mixed models is presented. A penalized regression approach is adopted in which tensor product smooths of several variables are constructed from smooths of each variable separately, these "marginal" smooths being represented using a low-rank basis with an associated quadratic wiggliness penalty. The smooths offer several advantages: (i) they have one wiggliness penalty per covariate and are hence invariant to linear rescaling of covariates, making them useful when there is no "natural" way to scale covariates relative to each other; (ii) they have a useful tuneable range of smoothness, unlike single-penalty tensor product smooths that are scale invariant; (iii) the relatively low rank of the smooths means that they are computationally efficient; (iv) the penalties on the smooths are easily interpretable in terms of function shape; (v) the smooths can be generated completely automatically from any marginal smoothing bases and associated quadratic penalties, giving the modeler considerable flexibility to choose the basis penalty combination most appropriate to each modeling task; and (vi) the smooths can easily be written as components of a standard linear or generalized linear mixed model, allowing them to be used as components of the rich family of such models implemented in standard software, and to take advantage of the efficient and stable computational methods that have been developed for such models. A small simulation study shows that the methods can compare favorably with recently developed smoothing spline ANOVA methods.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Functional Data Analysis

                Bookmark

                Author and article information

                Contributors
                Journal
                1868-6354
                Laboratory Phonology: Journal of the Association for Laboratory Phonology
                Ubiquity Press
                1868-6354
                18 March 2020
                2020
                : 11
                : 1
                : 2
                Affiliations
                [1 ]Speech, Hearing and Phonetic Sciences, University College London, UK
                [2 ]Institute of Phonetics and Speech Processing, Ludwig Maximilians Universität Munich, DE
                [3 ]Max Planck Institute for Biophysical Chemistry, Göttingen, DE
                [4 ]The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, AU
                Article
                10.5334/labphon.214
                0bf3623c-41b3-4612-a180-7eb14fb24e8d
                Copyright: © 2020 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 July 2019
                : 22 January 2020
                Categories
                Journal article

                Applied linguistics,General linguistics,Linguistics & Semiotics
                speech dynamics,FLMM,real-time MRI,GAMM,speech imaging

                Comments

                Comment on this article