Effects of Gradual and Sudden Introduction of Perturbations on Adaptive Responses to Formant-Shift and Formant-Clamp Perturbations.

PURPOSE: When the speech motor system encounters errors, it generates adaptive responses to compensate for the errors. Unlike errors induced by formant-shift perturbations, errors induced by formant-clamp perturbations do not correspond with the speaker's speech (i.e., degraded motor-to-auditory correspondence). We previously showed that adaptive responses to formant-clamp perturbations are smaller than responses to formant-shift perturbations when perturbations are introduced gradually. This study examined responses to formant-clamp and formant-shift perturbations when perturbations are introduced suddenly. METHOD: One group of participants (n = 30) experienced gradually introduced formant-clamp and formant-shift perturbations, and another group (n = 30) experienced suddenly introduced formant-clamp and formant-shift perturbations. We designed the perturbations based on participant-specific vowel configurations such that a participant's first and second formants of /ɛ/ were perturbed toward their /æ/. To estimate adaptive responses, we measured formant changes (0-100 ms of the vowel) in response to the formant perturbations. RESULTS: We found that (a) the difference between responses to formant-clamp and formant-shift perturbations was smaller when the perturbations were introduced suddenly and (b) responses to suddenly introduced (but not gradually introduced) formant-shift perturbations positively correlated with responses to formant-clamp perturbations. CONCLUSIONS: These results showed that the speech motor system responds to errors induced by formant-shift and formant-clamp perturbations more differently when perturbations are introduced gradually than suddenly. Overall, the quality of errors (formant-shift vs. formant-clamp) and the manner of introducing errors (gradually vs. suddenly) modulate the speech motor system's evaluations of and responses to errors. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.22406422.

Do Not Cut Off Your Tail: A Mega-Analysis of Responses to Auditory Perturbation Experiments.

PURPOSE: The practice of removing "following" responses from speech perturbation analyses is increasingly common, despite no clear evidence as to whether these responses represent a unique response type. This study aimed to determine if the distribution of responses to auditory perturbation paradigms represents a bimodal distribution, consisting of two distinct response types, or a unimodal distribution. METHOD: This mega-analysis pooled data from 22 previous studies to examine the distribution and magnitude of responses to auditory perturbations across four tasks: adaptive pitch, adaptive formant, reflexive pitch, and reflexive formant. Data included at least 150 unique participants for each task, with studies comprising younger adult, older adult, and Parkinson's disease populations. A Silverman's unimodality test followed by a smoothed bootstrap resampling technique was performed for each task to evaluate the number of modes in each distribution. Wilcoxon signed-ranks tests were also performed for each distribution to confirm significant compensation in response to the perturbation. RESULTS: Modality analyses were not significant (p > .05) for any group or task, indicating unimodal distributions. Our analyses also confirmed compensatory reflexive responses to pitch and formant perturbations across all groups, as well as adaptive responses to sustained formant perturbations. However, analyses of sustained pitch perturbations only revealed evidence of adaptation in studies with younger adults. CONCLUSION: The demonstration of a clear unimodal distribution across all tasks suggests that following responses do not represent a distinct response pattern, but rather the tail of a unimodal distribution. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24282676.

Compensatory Responses to Formant Perturbations Proportionally Decrease as Perturbations Increase.

Purpose We continuously monitor our speech output to detect potential errors in our productions. When we encounter errors, we rapidly change our speech output to compensate for the errors. However, it remains unclear whether we adjust the magnitude of our compensatory responses based on the characteristics of errors. Method Participants (N = 30 adults) produced monosyllabic words containing /ɛ/ (/hɛp/, /hɛd/, /hɛk/) while receiving perturbed or unperturbed auditory feedback. In the perturbed trials, we applied two different types of formant perturbations: (a) the F1 shift, in which the first formant of /ɛ/ was increased, and (b) the F1-F2 shift, in which the first formant was increased and the second formant was decreased to make a participant's /ɛ/ sound like his or her /æ/. In each perturbation condition, we applied three participant-specific perturbation magnitudes (0.5, 1.0, and 1.5 ɛ-æ distance). Results Compensatory responses to perturbations with the magnitude of 1.5 ɛ-æ were proportionally smaller than responses to perturbation magnitudes of 0.5 ɛ-æ. Responses to the F1-F2 shift were larger than responses to the F1 shift regardless of the perturbation magnitude. Additionally, compensatory responses for /hɛd/ were smaller than responses for /hɛp/ and /hɛk/. Conclusions Overall, these results suggest that the brain uses its error evaluation to determine the extent of compensatory responses. The brain may also consider categorical errors and phonemic environments (e.g., articulatory configurations of the following phoneme) to determine the magnitude of its compensatory responses to auditory errors.

Production Variability and Categorical Perception of Vowels Are Strongly Linked.

Theoretical models of speech production suggest that the speech motor system (SMS) uses auditory goals to determine errors in its auditory output during vowel production. This type of error calculation indicates that within-speaker production variability of a given vowel is related to the size of the vowel's auditory goal. However, emerging evidence suggests that the SMS may also take into account perceptual knowledge of vowel categories (in addition to auditory goals) to estimate errors in auditory feedback. In this study, we examined how this mechanism influences within-speaker variability in vowel production. We conducted a study (n = 40 adults), consisting of a vowel categorization task and a vowel production task. The vowel categorization task was designed-based on participant-specific vowels-to estimate the categorical perceptual boundary (CPB) between two front vowels (/ε/ and /æ/). Using the vowel production data of each participant, we calculated a variability-based boundary (VBB) located at the "center of mass" of the two vowels. The inverse of the standard deviation of a vowel distribution was used as the "mass" of the vowel. We found that: (a) categorical boundary was located farther from more variable vowels; and (b) the calculated VBB (i.e., the center of mass of the vowels) significantly and positively correlated with the estimated categorical boundary (r = 0.912 for formants calculated in hertz; r = 0.854 for formants calculated in bark). Overall, our findings support a view that vowel production and vowel perception are strongly and bidirectionally linked.

A Simple 3-Parameter Model for Examining Adaptation in Speech and Voice Production.

Sensorimotor adaptation experiments are commonly used to examine motor learning behavior and to uncover information about the underlying control mechanisms of many motor behaviors, including speech production. In the speech and voice domains, aspects of the acoustic signal are shifted/perturbed over time via auditory feedback manipulations. In response, speakers alter their production in the opposite direction of the shift so that their perceived production is closer to what they intended. This process relies on a combination of feedback and feedforward control mechanisms that are difficult to disentangle. The current study describes and tests a simple 3-parameter mathematical model that quantifies the relative contribution of feedback and feedforward control mechanisms to sensorimotor adaptation. The model is a simplified version of the DIVA model, an adaptive neural network model of speech motor control. The three fitting parameters of SimpleDIVA are associated with the three key subsystems involved in speech motor control, namely auditory feedback control, somatosensory feedback control, and feedforward control. The model is tested through computer simulations that identify optimal model fits to six existing sensorimotor adaptation datasets. We show its utility in (1) interpreting the results of adaptation experiments involving the first and second formant frequencies as well as fundamental frequency; (2) assessing the effects of masking noise in adaptation paradigms; (3) fitting more than one perturbation dimension simultaneously; (4) examining sensorimotor adaptation at different timepoints in the production signal; and (5) quantitatively predicting responses in one experiment using parameters derived from another experiment. The model simulations produce excellent fits to real data across different types of perturbations and experimental paradigms (mean correlation between data and model fits across all six studies = 0.95 ± 0.02). The model parameters provide a mechanistic explanation for the behavioral responses to the adaptation paradigm that are not readily available from the behavioral responses alone. Overall, SimpleDIVA offers new insights into speech and voice motor control and has the potential to inform future directions of speech rehabilitation research in disordered populations. Simulation software, including an easy-to-use graphical user interface, is publicly available to facilitate the use of the model in future studies.