Planting the seed for sound change: Evidence from real-time MRI of velum kinematics in German

Carignan, Christopher; Coretta, Stefano; Frahm, Jens; Harrington, Jonathan; Hoole, Phil; Joseph, Arun A.; Kunay, Esther; Voit, Dirk

doi:10.1353/lan.2021.0020

Cited by 23 publications

(14 citation statements)

References 113 publications

(119 reference statements)

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“…One of the main problems in developing articulatory models is the lack in articulatory data exhibiting a sufficient spatio-temporal resolution. Static MRI and X-ray (or CT) data give a sufficient spatial resolution in order to extract the surface shape of all articulators and vocal tract walls with a mean error of around 1 mm but a sufficient temporal resolution is only reached by some highly specialized research groups (e.g., Fu et al, 2017 ; Carignan et al, 2021 ). In case of midsagittal 2D-data a resolution of about 50 Hz (time interval of 20 msec) as is reported for cine X-ray tracking is not sufficient in case of tracking and modeling for example consonantal closing-opening movements.…”

Section: Discussion: Limitations and Future Directions For Modeling S...mentioning

confidence: 99%

“…Moreover, EMA receiver coils with its diameter of about 3 mm may influence articulation. Recently even cine MRI increases dramatically in temporal resolution up to 50 Hz (20 msec) with a spatial resolution of 1.4 mm including a post-processing resampling procedure leading to an even higher temporal resolution up to a factor 10 ( Carignan et al, 2021 ) or up to 166 Hz (6 msec) with a spatial resolution of 2.2 mm ( Fu et al, 2017 ).…”

Section: Discussion: Limitations and Future Directions For Modeling S...mentioning

confidence: 99%

“…Dynamic X-ray movement data (cine-radio data; cineradiography), i.e., a succession of vocal tract shapes for complete utterances are rare because of ethical reasons due to the high radiation exposure. MRI movement data (cine MRI)—which can be gathered with less health risks–are hard to acquire with a sufficient spatio-temporal resolution but are currently used more and more ( Harandi et al, 2017 ; Carignan et al, 2021 ). In order to check movement features of a model EMA data (electromagnetic articulography), EMA data in combination with EPG data (electropalatography), or X-ray microbeam data can be used but these data give only selective punctual spatial information concerning the vocal tract shape ( Westbury et al, 1990 ; Wrench and Hardcastle, 2000 ; Toutios et al, 2011 ).…”

Section: Features For Differentiating Computer-implemented Articulato...mentioning

confidence: 99%

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Computer-Implemented Articulatory Models for Speech Production: A Review

Kröger

2022

Front. Robot. AI

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Modeling speech production and speech articulation is still an evolving research topic. Some current core questions are: What is the underlying (neural) organization for controlling speech articulation? How to model speech articulators like lips and tongue and their movements in an efficient but also biologically realistic way? How to develop high-quality articulatory-acoustic models leading to high-quality articulatory speech synthesis? Thus, on the one hand computer-modeling will help us to unfold underlying biological as well as acoustic-articulatory concepts of speech production and on the other hand further modeling efforts will help us to reach the goal of high-quality articulatory-acoustic speech synthesis based on more detailed knowledge on vocal tract acoustics and speech articulation. Currently, articulatory models are not able to reach the quality level of corpus-based speech synthesis. Moreover, biomechanical and neuromuscular based approaches are complex and still not usable for sentence-level speech synthesis. This paper lists many computer-implemented articulatory models and provides criteria for dividing articulatory models in different categories. A recent major research question, i.e., how to control articulatory models in a neurobiologically adequate manner is discussed in detail. It can be concluded that there is a strong need to further developing articulatory-acoustic models in order to test quantitative neurobiologically based control concepts for speech articulation as well as to uncover the remaining details in human articulatory and acoustic signal generation. Furthermore, these efforts may help us to approach the goal of establishing high-quality articulatory-acoustic as well as neurobiologically grounded speech synthesis.

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Section: Discussion: Limitations and Future Directions For Modeling S...mentioning

confidence: 99%

Section: Discussion: Limitations and Future Directions For Modeling S...mentioning

confidence: 99%

Section: Features For Differentiating Computer-implemented Articulato...mentioning

confidence: 99%

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Computer-Implemented Articulatory Models for Speech Production: A Review

Kröger

2022

Front. Robot. AI

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“…Rather, the independent velar gesture, which was once part of the gestural constellation of /ŋ/, would then be "free" to shift to the temporal interval of the vowel, i.e., vowel nasalization. Decoupling of gestural sub-components of speech segments has been argued to play a key role in mechanisms of sound change, since such decoupling might permit independent temporal realignment (Beddor, 2009) and/or kinematic alteration (Carignan et al, 2021) of one of the decoupled gestures. This has further implications for possible mechanisms of sound change.…”

Section: Theoretical Implications Of the Studymentioning

confidence: 99%

An investigation of the dynamics of vowel nasalization in Arabana using machine learning of acoustic features

Carignan

Chen

Harvey

et al. 2023

Laboratory Phonology

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This paper presents exploratory research on temporally dynamic patterns of vowel nasalization from two speakers of Arabana. To derive a dynamic measure of nasality, we use gradient tree boosting algorithms to statistically learn the mapping between acoustics and vowel nasality in a speaker-specific manner. Three primary findings emerge: (1) NVN contexts exhibit nasalization throughout the entirety of the vowel interval, and we propose that a similar co-articulatory realization previously acted to resist diachronic change in this environment; (2) anticipatory vowel nasalization is nearly as extensive as carryover vowel nasalization, which is contrary to previous claims; and (3) the degree of vowel nasalization in word-initial contexts is relatively high, even in the #_C environment, suggesting that the sound change *#Na > #a has involved the loss of the oral constriction associated with N but not the complete loss of the velum gesture.

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“…A substantial body of experimental research has documented fine-grained (synchronic) phonetic biases in production and perception and highlighted parallels with attested sound changes in the same direction such as the development of contrastive nasalization (Beddor 2009;Carignan et al 2021), tonogenesis (Coetzee et al 2018;Kirby 2014); /u/-fronting (Harrington et al 2008), /l/-vocalization (Recasens 2012, Lin et al 2014, /s/-retraction ) and velar palatalisation (Guion 1998).…”

Section: Sound Change As the Accumulation Of Synchronic Phonetic Biasesmentioning

confidence: 99%

Individual variation and the coarticulatory path to sound change: agent-based modeling of /str/ in English and Italian

Stevens

Harrington

2022

Glossa: A Journal of General Linguistics

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This study is concerned with sound change in single populations. Sources differ as to whether such sound changes are attributed to the accumulation of coarticulatory variation, i.e. shared tendencies, or whether idiosyncratic variation is necessary to shift population norms. Using agent-based modeling, this study compares effects of interaction amongst members of single populations with (English-speaking) and without (Italian-speaking) coarticulatory variation involving /s/-retraction in /str/. After interaction, directional shifts /s/ -> /ʃ/ are found in English but not Italian /str/. Manipulating starting conditions to remove outlier individuals from the group of interacting agents has little effect on population-level outcomes. These results provide empirical support for the idea that sound change originates in phonetic variation that is common, rather than exceptional, in a population.

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Planting the seed for sound change: Evidence from real-time MRI of velum kinematics in German

Cited by 23 publications

References 113 publications

Computer-Implemented Articulatory Models for Speech Production: A Review

Computer-Implemented Articulatory Models for Speech Production: A Review

An investigation of the dynamics of vowel nasalization in Arabana using machine learning of acoustic features

Individual variation and the coarticulatory path to sound change: agent-based modeling of /str/ in English and Italian

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