scholarly journals Auditory feedback is used for adaptive control of timing in speech

2020 ◽  
Author(s):  
Robin Karlin ◽  
Benjamin Parrell ◽  
Chris Naber
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Auditory Feedback ◽  
Feedforward Control ◽  
Vowel Duration ◽  
Washout Phase ◽  
Vowel Formants ◽  
Temporal Perturbation ◽  
Online Feedback ◽  
Perturbation Magnitude

Real-time altered auditory feedback has demonstrated a key role for auditory feedback in both online feedback control and in updating feedforward control for future utterances. Much of this research has examined control in the spectral domain, and has found that speakers compensate for perturbations to vowel formants, intensity, and fricative center of gravity. The aim of the current study is to examine adaptation in response to temporal perturbation, using real-time perturbation of ongoing speech. Word-initial consonant targets (VOT for /k, g/ and fricative duration for /s, z/) were lengthened and the following stressed vowel (/æ/) was shortened. Overall, speakers did not adapt to lengthened consonants, but did lengthen vowels by nearly 100\% of the perturbation magnitude in response to shortening. Vowel lengthening showed continued aftereffects during a washout phase when perturbation was abruptly removed. Although speakers did not actively adapt consonant durations, the adaptation in vowel duration leads to the consonant taking up an overall smaller proportion of the syllable, aligning with previous research that suggests that speakers attend to proportional durations rather than absolute durations. These results indicate that speakers actively monitor duration and update upcoming plans accordingly.

scholarly journals Auditory Feedback Is Used for Adaptation and Compensation in Speech Timing

2021 ◽  
pp. 1-21
Author(s):  
Robin Karlin ◽  
Chris Naber ◽  
Benjamin Parrell
Keyword(s):  
Real Time ◽  
Auditory Feedback ◽  
Temporal Dynamics ◽  
Voice Onset Time ◽  
Feedforward Control ◽  
Onset Time ◽  
Baseline Phase ◽  
Washout Phase ◽  
Early Portion ◽  
Temporal Perturbation

Purpose Real-time altered feedback has demonstrated a key role for auditory feedback in both online feedback control and in updating feedforward control for future utterances. The aim of this study was to examine adaptation in response to temporal perturbation using real-time perturbation of ongoing speech. Method Twenty native English speakers with no reported history of speech or hearing disorders participated in this study. The study consisted of four word blocks, using the phrases “a capper,” “a gapper,” “a sapper,” and “a zapper” (due to issues with the implementation of perturbation, “gapper” was excluded from analysis). In each block, participants completed a baseline phase (30 trials of veridical feedback), a ramp phase (feedback perturbation increasing to maximum over 30 trials), a hold phase (60 trials with perturbation held at maximum), and a washout phase (30 trials, feedback abruptly returned to veridical feedback). Word-initial consonant targets (voice onset time for /k, g/ and fricative duration for /s, z/) were lengthened, and the following stressed vowel (/æ/) was shortened. Results Overall, speakers did not adapt the production of their consonants but did lengthen their vowel production in response to shortening. Vowel lengthening showed continued aftereffects during the early portion of the washout phase. Although speakers did not adapt absolute consonant durations, consonant duration was reduced as a proportion of the total syllable duration. This is consistent with previous research that suggests that speakers attend to proportional durations rather than absolute durations. Conclusion These results indicate that speakers actively monitor proportional durations and update the temporal dynamics of planning units extending beyond a single segment.

scholarly journals Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 420
Author(s):  
Phong B. Dao
Keyword(s):  
Real Time ◽  
Feedforward Control ◽  
Design Method ◽  
Mechatronic Systems ◽  
Control Approach ◽  
Runtime Environment ◽  
Multiagent Control ◽  
On Line ◽  
Promising Solution ◽  
Function Approximator

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems

2021 ◽  
pp. 1-1
Author(s):  
Duc M. Le ◽  
Max L. Greene ◽  
Wanjiku A. Makumi ◽  
Warren E. Dixon
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Real Time ◽  
Deep Neural Network

Real-time stable adaptive control implementation using a neural network processor

1995 ◽  
Author(s):  
Timothy Robinson ◽  
Mohammad Bodruzzaman ◽  
Kevin L. Priddy ◽  
Karl Mathia
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Network Processor ◽  
Control Implementation
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