An EMG analysis of sequential control cycles of articulatory activity during / ǝp Vp/ utterances

Honda, Kiyoshi; Kusakawa, Naoki; Kakita, Yuki

doi:10.1016/s0095-4470(19)30253-0

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Note that we did this set of correlations separately for each of the two EMG signals, rather than doing a series of multiple regression analyses as in Boves (1992, 1995), because earlier studies have suggested rather different MRT values for the two muscles, and doing a multiple regression analysis for each combination of CT lead time and SH lead time would have meant doing more tests than we have tokens. See Honda, Kusakawa, and Kakita (1992) for a previous modeling study of multiple simultaneous EMG traces, which points out the marked asynchrony among events extracted from different extrinsic lingual muscles and two accessory tongue muscles.…”

Section: Figurementioning

confidence: 99%

Linguistic Models of F0 Use, Physiological Models of F0 Control, and the Issue of “Mean Response Time”

Herman

Beckman²,

Honda

1999

Lang Speech

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This paper evaluates "mean response time" (MRT), a method used in previous studies to relate physiological evidence (recordings of electromyographic activity in the cricothyroid and sternohyoid) to acoustic evidence (fundamental frequency). Rather than averaging over tokens before correlating these signals, we calculated the best response time (RT) for each token, and evaluated the pattern of variability across utterances. Furthermore, rather than correlating over whole utterances, we correlated electromyographic activity (EMG) to fundamental frequency (F0) only over intervals defined in terms of linguistically significant events in the F0 trace, identified using a linguistically motivated model of English intonation. Steep changes in the F0 tended to have better correlation coefficients than shallow ones, which we relate to the physiological model by noting the complex of components contributing to both signal types. Also, the distribution of lead times was easier to interpret when the two tones delimiting the analysis domain had some tight temporal relationship specified by the intonational phonology. Finally, lead times tended to vary as a function of what preceded the target rise or fall. In short, averaging over signals before analysis obscures patterns of variation in the data which may lead to new insights and to new directions for research.

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Section: Figurementioning

confidence: 99%

Linguistic Models of F0 Use, Physiological Models of F0 Control, and the Issue of “Mean Response Time”

Herman

Beckman²,

Honda

1999

Lang Speech

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A neural-network model enabling sensorimotor learning: Application to the control of arm movements and some implications for speech-motor control and stuttering

Kalveram

1993

Psychol. Res

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Low-level motor control is defined as adapting an organism to the unique physical properties of its own limbs. The two-jointed arm serves to exemplify that effective low-level motor control demands a neurally medicated inversion of the dynamics, as well as of the kinematics, of a limb system. Reflex-like processing--that is, feedforword of either actual or predicted proprioceptive signals--is thereby assumed to be the principle of the dynamics control. As regards speech-motor control, the overall tool transformation is assumed to transform the force pattern of the articulatory muscles into speech sounds. Like the arm model, the vocal-tract transformation thus defined is also divided into two parts, namely the transformation relating the muscle forces to the mechanospatial states of the vocal tract (which is analogous to the forward dynamics including natural interarticulatory couplings), and the transformation relating the mechanospatial states to the speech sounds. Low-level speech-motor control, then, needs to invert both transformations, each of which can be learned by means of the self-imitation algorithm. Erroneous learning can fail to decouple interarticulatory coupling and therefore lead to abnormal feedback loops through the reflex-like operating neural network, which in turn can cause stuttering if audiophonatoric coupling is involved in learning.

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Tongue, Jaw, and Lip Muscle Activity and Jaw Movement during Experimental Chewing Efforts in Man

et al. 1996

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The electromyographic (EMG) activity of the human genioglossus (GG) muscle during chewing efforts is not fully understood. In this study, the EMG activity of the human GG muscle during unilateral gum chewing was illustrated and correlated with the activities in the anterior temporalis (AT), the anterior digastric (DG), and the inferior orbicularis oris (OI) muscles. GG muscle activity was measured with customized surface electrodes, while other muscles were recorded with conventional surface electrodes. EMG activities during tongue displacement and the articulation of long vowels, recorded by the customized electrodes, were consistent with the recordings obtained by fine wire electrodes placed in the GG muscle. Jaw displacement was monitored by means of a kinesiograph with a transducer attached to the mandibular central incisors. Mean normalized GG muscle activity showed an onset in the last one-fifth of the intercuspal phase, gradually increasing during jaw-opening, and at its greatest immediately before the maximum jaw-opening position. It then decreased during jaw-closing and ceased in intercuspation but showed a small rebound in the third fifth of the intercuspal phase. The GG muscle burst showed phase lags with the DG and OI muscles and an opposite phase with the AT muscle (all P < 0.0001). All correlations were statistically significant (all P < 0.0001, r values between 0.88 and 0.97). The results suggest central coordination of the timing of the activities of the jaw, lip, and tongue muscles in chewing.

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An EMG analysis of sequential control cycles of articulatory activity during / ǝp Vp/ utterances

Cited by 3 publications

References 8 publications

Linguistic Models of F0 Use, Physiological Models of F0 Control, and the Issue of “Mean Response Time”

Linguistic Models of F0 Use, Physiological Models of F0 Control, and the Issue of “Mean Response Time”

A neural-network model enabling sensorimotor learning: Application to the control of arm movements and some implications for speech-motor control and stuttering

Tongue, Jaw, and Lip Muscle Activity and Jaw Movement during Experimental Chewing Efforts in Man

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