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. 2009 Dec;52(6):1595-609.
doi: 10.1044/1092-4388(2009/07-0283).

Computational neural modeling of speech motor control in childhood apraxia of speech (CAS)

Hayo Terband  1 , Ben Maassen, Frank H Guenther, Jonathan Brumberg
Affiliations

Computational neural modeling of speech motor control in childhood apraxia of speech (CAS)

Hayo Terband et al. J Speech Lang Hear Res. 2009 Dec.

Abstract

Purpose: Childhood apraxia of speech (CAS) has been associated with a wide variety of diagnostic descriptions and has been shown to involve different symptoms during successive stages of development. In the present study, the authors attempted to associate the symptoms of CAS in a particular developmental stage with particular information-processing deficits by using computational modeling with the Directions Into Velocities of Articulators (DIVA) model. The hypothesis was that the speech production system in CAS suffers from poor feed-forward control and, consequently, an increased reliance on the feedback control subsystem.

Method: In a series of computer simulations, the authors systematically varied the ratio between feed-forward and feedback control during production attempts in the acquisition of feed-forward motor commands. The simulations were evaluated acoustically on 4 selected key symptoms of CAS.

Results: Results showed that increasing the reliance on feedback control causes increased severity of these 4 symptoms of CAS: deviant coarticulation, speech sound distortion, searching articulation, and increased variability.

Conclusions: The findings support the idea that the key symptoms found in CAS could result from an increased reliance on feedback control due to poor feed-forward commands. Two possible root causes of degraded feed-forward control in CAS are discussed: reduced somatosensory information and increased levels of neural noise.

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Figures

Fig. 1
Fig. 1
Schematic representation of the Directions Into Velocities of Articulators (DIVA) model of speech motor control. Projections to and from the cerebellum are simplified for clarity (Guenther et al., 2006). Sup. = Superior; Inf. = Inferior.
Fig. 2
Fig. 2
Schematic representation of the speech sound target for /abi/. The light gray columns indicate the measurement points. At each of these points, the mean formant value was calculated over a 30-ms time window (three measurements with 10-ms time intervals).
Fig. 3
Fig. 3
Example of coarticulation: The F2 values of V1 and C differ depending on V2. V = vowel; C = consonant.
Fig. 4
Fig. 4
Anticipatory (ANT) and carryover (CO) coarticulation for V1, C, and V2 in relation to feed-forward/feedback ratio.
Fig. 5
Fig. 5
Anticipatory (left panel) and carryover (right panel) coarticulation for different feed-forward/feedback ratios.
Fig. 6
Fig. 6
Speech sound distortion in relation to feed-forward/feedback ratio.
Fig. 7
Fig. 7
Groping: variability over the course of the production of speech sounds in relation to feed-forward/feedback ratio.
Fig. 8
Fig. 8
Token-to-token variability: mean variability of mean formant frequencies (left panel) and mean standard deviation of the coarticulation (COART), speech sound distortion (SSD), and groping or searching articulatory behavior (G/SAB) indices (right panel) between productions of speech sounds.
See this image and copyright information in PMC

References

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