Dataset Information

Whistling shares a common tongue with speech: bioacoustics from real-time MRI of the human vocal tract.

ABSTRACT: Most human communication is carried by modulations of the voice. However, a wide range of cultures has developed alternative forms of communication that make use of a whistled sound source. For example, whistling is used as a highly salient signal for capturing attention, and can have iconic cultural meanings such as the catcall, enact a formal code as in boatswain's calls or stand as a proxy for speech in whistled languages. We used real-time magnetic resonance imaging to examine the muscular control of whistling to describe a strong association between the shape of the tongue and the whistled frequency. This bioacoustic profile parallels the use of the tongue in vowel production. This is consistent with the role of whistled languages as proxies for spoken languages, in which one of the acoustical features of speech sounds is substituted with a frequency-modulated whistle. Furthermore, previous evidence that non-human apes may be capable of learning to whistle from humans suggests that these animals may have similar sensorimotor abilities to those that are used to support speech in humans.

SUBMITTER: Belyk M

PROVIDER: S-EPMC6784732 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Whistling shares a common tongue with speech: bioacoustics from real-time MRI of the human vocal tract.

Belyk Michel M Schultz Benjamin G BG Correia Joao J Beal Deryk S DS Kotz Sonja A SA

Proceedings. Biological sciences 20190925 1911

Most human communication is carried by modulations of the voice. However, a wide range of cultures has developed alternative forms of communication that make use of a whistled sound source. For example, whistling is used as a highly salient signal for capturing attention, and can have iconic cultural meanings such as the catcall, enact a formal code as in boatswain's calls or stand as a proxy for speech in whistled languages. We used real-time magnetic resonance imaging to examine the muscular c ...[more]

PMID: 31551056

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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

Whistling shares a common tongue with speech: bioacoustics from real-time MRI of the human vocal tract.

Publications

Whistling shares a common tongue with speech: bioacoustics from real-time MRI of the human vocal tract.

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