Speech Production

Latest papers

8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

A low-dimensional dynamical basis for tongue shape control
Oct 16, 2026Sam Kirkham, Patrycja StrycharczukSpeech Production Latent Dynamics Modeling
Phonetics Laboratory, Lancaster University, United Kingdom · Linguistics and English Language, University of Manchester, United Kingdom
Word meaning co-determines vowel-inherent spectral change. A corpus-based investigation of conversational Mandarin
Jul 23, 2026Xiaoyun Jin, Mirjam Ernestus, R. Harald BaayenSpeech Production
Quantitative Linguistics, Eberhard Karls Universität Tübingen, 72074 Tübingen, Germany · Center for Language Studies, Radboud University, 6525 HT Nijmegen, The Netherlands
A 1000-hour EEG-EMG-audio dataset of Japanese speech production
May 31, 2026Motoshige Sato, Ilya Horiguchi, Masakazu Inoue +6Electroencephalography EEG-to-Text Decoding
Araya Inc., Tokyo, Japan
Articulatory strategy as a source of variation in acoustic vowel dynamics
May 22, 2026Patrycja Strycharczuk, Justin J. H. Lo, Sam KirkhamSpeech Production
Linguistics and English Language, University of Manchester, United Kingdom · Linguistics and English Language, Lancaster University, United Kingdom
Structure from rank: Rank-order coding as a bridge from sequence to structure
Mar 9, 2026Xiaodan Chen, Alexandre Pitti, Mathias Quoy +1Speech Production Sequence Modeling
ETIS, CY Cergy-Paris Université - ENSEA – CNRS, UMR 8051, 2 Av. Adolphe Chauvin, Pontoise, 95300, Ile de France, France · A*Star, 1 Fusionopolis Way, #20-10 Connexis North Tower, Singapore, 138632, Singapore · IPAL (International Research Laboratory on Artiﬁcial Intelligence), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore, 138632, Singapore
An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production
Mar 5, 2026Jihwan Lee, Parsa Razmara, Kevin Huang +16Neuroimaging Speech Production
Signal Analysis and Interpretation Laboratory, University of Southern California · Ming Hsieh Dept. of Electrical and Computer Engineering, University of Southern California · Dept. of Linguistics, University of Southern California
Automated Measurement of Geniohyoid Muscle Thickness During Speech Using Deep Learning and Ultrasound
Feb 26, 2026Alisher Myrgyyassov, Bruce Xiao Wang, Yu Sun +4Speech Production Ultrasound Image Segmentation
Biomedical Engineering Department, Hong Kong Polytechnic University, Hong Kong, China · English and Communication Department, Hong Kong Polytechnic University, Hong Kong, China · Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong, China +1
A mathematical model of the vowel space
Oct 19, 2021Frédéric BerthommierSpeech Processing Speech Production
GIPSA-PCMD

Oct 16, 2026J/K move · Enter open · S save

A low-dimensional dynamical basis for tongue shape control

Sam Kirkham, Patrycja Strycharczuk

Phonetics Laboratory, Lancaster University, United Kingdom · Linguistics and English Language, University of Manchester, United Kingdom

Speech Production Latent Dynamics Modeling

Open paper AskPDF arXiv

Speech production involves the control of a high-dimensional articulatory system with many degrees-of-freedom, but a widespread hypothesis is that speakers navigate this complexity using a set of lower-dimensional control variables. Common candidates for these control variables include individual articulators (e.g. tongue body) and abstract tract variables (e.g. tongue body constriction location), but identifying the most appropriate coordinate system for articulatory control remains an open question. In this study, we advance a model in which holistic tongue shape modes represent a potential low-dimensional control basis for a simple dynamical law. We first discover empirical tongue shape modes in a cross-speaker coordinate space and then fit the model to ultrasound data on monophthongal vowels from 36 speakers of British English. A three-dimensional linear point attractor fitted to these time-varying modes recovers the dynamics of tongue shape with a high degree of accuracy. This suggests that complex tongue movement can arise from independent modes that linearly combine under simple dynamical laws, thereby relocating some complexity from the control signal to the coordinate system. We show that this framing explains why one-dimensional articulatory measures can exhibit observable nonlinearities, and hypothesize that shape modes are a plausible kinematic expression of underlying muscle synergies.