LINGUIST List 31.1958

Mon Jun 15 2020

Confs: Comp Ling, Phonetics, Phonology/Online

Editor for this issue: Lauren Perkins <laurenlinguistlist.org>



Date: 12-Jun-2020
From: Tomas Lentz <lentzuva.nl>
Subject: Neural network models for articulatory gestures
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Neural network models for articulatory gestures
Short Title: NNArt


Date: 09-Jul-2020 - 09-Jul-2020
Location: Vancouver, BC, Canada
Contact: Tomas Lentz
Contact Email: < click here to access email >
Meeting URL: https://staff.science.uva.nl/t.o.lentz/nnart/

Linguistic Field(s): Computational Linguistics; Phonetics; Phonology

Meeting Description:

This workshop (satellite to LabPhon 17 on the day after, 9 July, 2020, 1:30pm-17:00pm) aims at bringing together researchers interested in articulation and computational modelling, especially neural networks.

Articulation has been formalised as dynamic articulatory gestures, i.e., a target-driven pattern of articulator movements (e.g., Browman & Goldstein, 1986). Such a pattern unfolds in time and space and could therefore also be seen as a spatial sequence of analytically relevant articulatory landmarks such as timepoint of peak velocity and target achievement. Seeing such sequences as sequences of vectors (of spatial coordinates) make them potentially learnable with algorithms for sequence modelling.

Current developments of machine learning offer greatly improved power for sequence learning and prediction. Recurrent Neural Networks (RNNs) or their extension Long Short-Term Memory (LSTM, Hochreiter & Schmidhuber, 1997) allows efficient training over short and even long time intervals (Gers, Schraudolph & Schmidhuber, 2002). Such networks have been used for acoustic modelling, but their application in articulation research has been mainly been limited to ultrasound data, and applied less to the classification of two-dimensional articulator movement curves as obtained from EMA or ROI analyses of MRI data.

However, promising approaches to acoustics-to-EMA mapping tentatively suggest that articulatory movement allow meaningful modelling using deep neural networks (e.g., Liu et al., 2005, Chartier et al., 2018)

Program Information:

NNART offers three pre-recorded presentations of 30 minutes, available during (and through) the main LabPhon conference, and one online discussion session on 9 July, 12:45pm-1:30pm (Vancouver time).

Presentations (prerecorded):
- Sam Tilsen, Learning gestural parameters and activation in an RNN implementation of Task Dynamics
- Sam Kirkham, Georgina Brown and Emily Gorman, Uncovering phonological invariance and speaker individuality in articulatory gestures using machine learning
- Marco Silva Fonseca and Brennan Dell, Modelling optional sound variation and obligatory gesture assimilation using LSTM RNNs

Note: This workshop is accessible to registered attendees of the online conference LabPhon 17. Due to the worldwide Covid-19 pandemic, both the main conference and our satellite will go virtual instead of taking place in Vancouver. The presentations will be made available as video files.

Discussion session (live, using Zoom):
You can send questions on the presentations, or discussion topics, to the organizers (details see above), or ask them in person to the presenters at the online session. Please register for both the main conference and our workshop to be kept up to date and to receive further information (e.g., the Zoom link) for the discussion session.




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