Motor Equivalence in Speech Production

Perrier, Pascal; Fuchs, Susanne

doi:10.1002/9781118584156.ch11

Cited by 23 publications

(21 citation statements)

References 47 publications

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“…Importantly, FACTS produces compensation auditory perturbations despite having no auditory targets. Previously, such compensation has been seen as evidence in favor of the existence of auditory targets for speech 14 . In FACTS, auditory perturbations cause a change in the estimated state of the vocal tract on which the task-level and articulatory-level feedback controllers operate.…”

Section: Model Response To Mechanical and Auditory Perturbationsmentioning

confidence: 97%

“…The magnitude of these responses only partially compensates for the perturbation, unlike the complete responses produced for mechanical perturbations. While the exact reason for this partial compensation is not known, it has been hypothesized to relate to small feedback gains 19 or conflict with the somatosensory feedback system 14 . We explore the cause of this partial compensation below, but focus here on the ability of the The produced F1 is shown in black and the perceived F1 is shown in blue.…”

Section: Model Response To Mechanical and Auditory Perturbationsmentioning

confidence: 99%

“…One approach, the equilibrium point hypothesis [9][10][11] , relegates feedback control to short-latency spinal or brainstem circuits operating on proprioceptive feedback, with high-level control based on pre-planned feedforward motor commands. Speech models of this type, such as the GEPPETO model, are able to reproduce many biomechanical aspects of speech but are not sensitive to auditory feedback [12][13][14][15][16] . Another approach is to combine feedback and feedforward controllers operating in parallel 17,18 .…”

Section: Introductionmentioning

confidence: 99%

“…Together, these results strongly indicate that the goal of speech is not to achieve desired positions of each individual speech articulator, but must rather be to achieve some higher-level goal. While most models of speech motor production thus implement control at a higher speech-relevant level, the precise nature of these goals (vocal tract constrictions [32][33][34] , auditory patterns 2, 12, 21 , or both 14,22 ) remains an ongoing debate.…”

Section: Introductionmentioning

confidence: 99%

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The FACTS model of speech motor control: fusing state estimation and task-based control

Parrell

Ramanarayanan

Nagarajan

et al. 2019

Preprint

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We present a new computational model of speech motor control: the Feedback-Aware Control of Tasks in Speech or FACTS model. This model is based on a state feedback control architecture, which is widely accepted in non-speech motor domains. The FACTS model employs a hierarchical observer-based architecture, with a distinct higher-level controller of speech tasks and a lower-level controller of speech articulators. The task controller is modeled as a dynamical system governing the creation of desired constrictions in the vocal tract, based on the Task Dynamics model. Critically, both the task and articulatory controllers rely on an internal estimate of the current state of the vocal tract to generate motor commands. This internal state estimate is derived from initial predictions based on efference copy of applied controls. The resulting state estimate is then used to generate predictions of expected auditory and somatosensory feedback, and a comparison between predicted feedback and actual feedback is used to update the internal state prediction. We show that the FACTS model is able to qualitatively replicate many characteristics of the human speech system: the model is robust to noise in both the sensory and motor pathways, is relatively unaffected by a loss of auditory feedback but is more significantly impacted by the loss of somatosensory feedback, and responds appropriately to externally-imposed alterations of auditory and somatosensory feedback. The model also replicates previously hypothesized trade-offs between reliance on auditory and somatosensory feedback in speech motor control and shows for the first time how this relationship may be mediated by acuity in each sensory domain. These results have important implications for our understanding of the speech motor control system in humans.

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Section: Model Response To Mechanical and Auditory Perturbationsmentioning

confidence: 97%

Section: Model Response To Mechanical and Auditory Perturbationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The FACTS model of speech motor control: fusing state estimation and task-based control

Parrell

Ramanarayanan

Nagarajan

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…From present-day research, we further obtain that although there is confirming evidence that lower vocal tract dynamics play a role in prosodic stress, it is also found that respiratory kinematic modulations are not always consistently recruited when performing some stressed vocalization (Fuchs, Koenig, & Petrone, 2019;Petrone, Fuchs, & Koenig, 2017). Indeed, akin to what is known about coordinative structures in general (Kugler, Scott Kelso, & Turvey, 1980), and speech specifically (Kelso, Tuller, Vatikiotis-Bateson, & Fowler, 1984), chest-wall modulations are just a few of many degrees of freedom that can be utilized to successfully perform a vocal act, next to a host of other vocally equivalent solutions (Perrier & Fuchs, 2015).…”

mentioning

confidence: 74%

Energy flows in gesture-speech physics: Exploratory findings and pre-registration of confirmatory analysis

Pouw¹,

Sa²,

Ne³

et al. 2019

Preprint

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A well-known phenomenon of multimodal language is the synchronous coupling of prosodic contours in speech with salient kinematic changes in co-speech hand-gesture motions. Invariably, such coupling has been rendered by psychologists to require a dedicated neural-cognitive mechanism preplanning speech and gesture trajectories. Recently, in a continuous vocalization task, it was found that acoustic peaks unintentionally appear in vocalizations when gesture motions reach peaks in physical impetus, suggesting a biomechanical basis for gesture-speech synchrony (Pouw, Harrison, & Dixon, 2019). However, from this rudimentary study it is still difficult to draw strong conclusions about gesture-speech dynamics in (more) complex speech and the precise biomechanical nature of these effects. Here we assess how the timing of physical impetus of a gesture relates to its effect on acoustic parameters of mono-syllabic consonant-vowel (CV) vocalization(/pa/). Furthermore, we assess how chest-wall kinematics is affected by gesturing, and whether this modulates the effect of gestures on acoustics. In the current exploratory analysis, we analyze a subset (N = 4) of an already collected dataset (N = 36), which serves as the basis for a pre-registration of the confirmatory analyses yet to be completed. Here we provide exploratory evidence that gestures affect acoustics (amplitude envelope and F0) as well as chest-wall kinematics during mono-syllabic vocalizations. These effects are more extreme when a gesture’s peak impetus occurs closer to the center of the vowel vocalization event. If the current findings can be replicated in confirmatory fashion, there is a more compelling case to be made that gesture-speech physics is important facet of multimodal synchrony.

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A Novel Hierarchical Framework for Measuring the Complexity and Irregularity of Multimodal Speech Signals and Its Application in the Assessment of Speech Impairment in Amyotrophic Lateral Sclerosis

Rong

2021

J Speech Lang Hear Res

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Purpose The purposes of this study are to develop a novel multimodal framework for measuring variability at the muscular, kinematic, and acoustic levels of the motor speech hierarchy and evaluate the utility of this framework in detecting speech impairment in amyotrophic lateral sclerosis (ALS). Method The myoelectric activities of three bilateral jaw muscle pairs (masseter, anterior temporalis, and anterior belly of digastric), jaw kinematics, and speech acoustics were recorded in 13 individuals with ALS and 10 neurologically healthy controls during sentence reading. Thirteen novel measures (six muscular, three kinematic, four acoustic), which characterized two different but interrelated aspects of variability—complexity and irregularity—were derived using linear and nonlinear methods. Exploratory factor analysis was applied to identify the latent factors underlying these measures. Based on the latent factors, three supervised classifiers—support vector machine (SVM), random forest (RF), and logistic regression (Logit)—were used to differentiate between the speech samples for patients and controls. Results Four interpretable latent factors were identified, representing the complexity of jaw kinematics, the irregularity of jaw antagonists functioning, the irregularity of jaw agonists functioning, and the irregularity of subband acoustic signals, respectively. Based on these latent factors, the speech samples for patients and controls were classified with high accuracy (> 96% for SVM and RF; 88.64% for Logit), outperforming the unimodal measures. Two factors showed significant between-groups differences, as characterized by decreased complexity of jaw kinematics and increased irregularity of jaw antagonists functioning in patients versus controls. Conclusions Decreased complexity of jaw kinematics presumably reflects impaired fine control of jaw movement, while increased irregularity of jaw antagonists functioning could be attributed to reduced synchronization of motor unit firing in ALS. The findings provide preliminary evidence for the utility of the multimodal framework as a novel quantitative assessment tool for detecting speech impairment in ALS and (potentially) in other neuromotor disorders.

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Motor Equivalence in Speech Production

Cited by 23 publications

References 47 publications

The FACTS model of speech motor control: fusing state estimation and task-based control

The FACTS model of speech motor control: fusing state estimation and task-based control

Energy flows in gesture-speech physics: Exploratory findings and pre-registration of confirmatory analysis

A Novel Hierarchical Framework for Measuring the Complexity and Irregularity of Multimodal Speech Signals and Its Application in the Assessment of Speech Impairment in Amyotrophic Lateral Sclerosis

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