Improving Oral Reading Rhythm by a Pet-like Robot
Date Issued
2010
Date
2010
Author(s)
Liu, Yen-Chi
Abstract
In this study, the pet-like dog that interacts with reader for enhancing the reading cadenced is being developed. Acoustic behaviors of voice signals are recorded to analyze the condition during interaction. The reader reading rhythm is found by endpoint detector. The endpoint detector is a classifier and based on the Hidden Markov Model. It is trained by nonverbal auditory cues, such as zero-crossing rate, energy, spectrum, and autocorrelation. The spectrum and autocorrelation are chosen to recognize the periodic signal.
To provide guidance of oral reading, the robot’s tail is developed into a two directions metronome by the two perpendicular motors. The goal is to classify reading states of the user, so the phase model of the words’ period in a sentence is defined, then the synchronization and rhythm parameter are defined by word phases. The synchronization parameter characterizes the users’ response models with respect to the tail input. And the rhythm of the sentence is characterized by the rhythm parameter. Measuring the rhythm parameter, if the rhythm parameter is far from the commanded rhythm, a new pace is then set up to control the metronome for the sentence reading guidance. The guidance rule for a specific user considers not only the rhythm parameter difference but also the synchronization parameter of the user.
Experiments were conducted to demonstrate the effect of guidance on oral reading performance. Rhythm parameters being controlled to approach better reading fluency is observed under the proposed guidance rule.
Subjects
oral reading
rhythm
control
hidden Markov model
robot
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