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Learning and adaptation in speech production without a vocal tract.

ABSTRACT: How is the complex audiomotor skill of speaking learned? To what extent does it depend on the specific characteristics of the vocal tract? Here, we developed a touchscreen-based speech synthesizer to examine learning of speech production independent of the vocal tract. Participants were trained to reproduce heard vowel targets by reaching to locations on the screen without visual feedback and receiving endpoint vowel sound auditory feedback that depended continuously on touch location. Participants demonstrated learning as evidenced by rapid increases in accuracy and consistency in the production of trained targets. This learning generalized to productions of novel vowel targets. Subsequent to learning, sensorimotor adaptation was observed in response to changes in the location-sound mapping. These findings suggest that participants learned adaptable sensorimotor maps allowing them to produce desired vowel sounds. These results have broad implications for understanding the acquisition of speech motor control.

SUBMITTER: Thompson MMC

PROVIDER: S-EPMC6753102 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Learning and adaptation in speech production without a vocal tract.

Thompson Megan M C MMC Houde John F JF Nagarajan Srikantan S SS

Scientific reports 20190919 1

How is the complex audiomotor skill of speaking learned? To what extent does it depend on the specific characteristics of the vocal tract? Here, we developed a touchscreen-based speech synthesizer to examine learning of speech production independent of the vocal tract. Participants were trained to reproduce heard vowel targets by reaching to locations on the screen without visual feedback and receiving endpoint vowel sound auditory feedback that depended continuously on touch location. Participa ...[more]

PMID: 31537824

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Project description:Background and objectiveMagnetic resonance (MR) imaging is increasingly used in studies of speech as it enables non-invasive visualisation of the vocal tract and articulators, thus providing information about their shape, size, motion and position. Extraction of this information for quantitative analysis is achieved using segmentation. Methods have been developed to segment the vocal tract, however, none of these also fully segment any articulators. The objective of this work was to develop a method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech, thus overcoming the limitations of existing methods.MethodsFive speech MR image sets (392 MR images in total), each of a different healthy adult volunteer, were used in this work. A fully convolutional network with an architecture similar to the original U-Net was developed to segment the following six regions in the image sets: the head, soft palate, jaw, tongue, vocal tract and tooth space. A five-fold cross-validation was performed to investigate the segmentation accuracy and generalisability of the network. The segmentation accuracy was assessed using standard overlap-based metrics (Dice coefficient and general Hausdorff distance) and a novel clinically relevant metric based on velopharyngeal closure.ResultsThe segmentations created by the method had a median Dice coefficient of 0.92 and a median general Hausdorff distance of 5mm. The method segmented the head most accurately (median Dice coefficient of 0.99), and the soft palate and tooth space least accurately (median Dice coefficients of 0.92 and 0.93 respectively). The segmentations created by the method correctly showed 90% (27 out of 30) of the velopharyngeal closures in the MR image sets.ConclusionsAn automatic method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech was successfully developed. The method is intended for use in clinical and non-clinical speech studies which involve quantitative analysis of the shape, size, motion and position of the vocal tract and articulators. In addition, a novel clinically relevant metric for assessing the accuracy of vocal tract and articulator segmentation methods was developed.

Dataset Information

Learning and adaptation in speech production without a vocal tract.

Publications

Learning and adaptation in speech production without a vocal tract.

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