Articulatory coordination distinguishes complex segments from segment sequences

Shaw, Jason A.; Oh, Se-Jin; Durvasula, Karthik; Kochetov, Alexei

doi:10.1017/s0952675721000269

Cited by 14 publications

(2 citation statements)

References 59 publications

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“…Their learner calculates the co-occurrence probability of two neighboring sounds to determine whether they are a single segment or two. Shaw et al (2021) approached the segmentation problem from the perspective of articulatory gesture timing.…”

Section: Introductionmentioning

confidence: 99%

The Segment Status of the Mandarin Glide: A Language Game Experiment

2023

AMP

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The acquisition of a phonological grammar requires the segmentation of an utterance into individual consonants and vowels as a first step, yet it is often taken as a given. I show that segmentation is not a trivial problem by drawing evidence from Mandarin Chinese, where the shortage of morphophonological processes leads to ambiguity in the segmentation of prenuclear glides. I present two language game experiments, in which Mandarin speakers are asked to disassemble syllables in their language, thus revealing their segmental structure. The task, based on fanqie secret languages, involves taking a disyllabic word and swapping its two onsets, in order to form a codeword. What the speaker does with the glide informs us on how they have segmented the sound. A key finding of the experiments is that /j/ is more likely to be treated as an independent segment after non-palatal onsets, compared to palatal ones. Speaker variation, both interspeaker and intraspeaker, is observed. Nevertheless, Mandarin speakers converge on three distinct types.

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Section: Introductionmentioning

confidence: 99%

The Segment Status of the Mandarin Glide: A Language Game Experiment

2023

AMP

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“…EMA [ 39 , 40 , 41 ] is useful to localize the movement within the vocal tract by using electromagnetic transmitter coils to track the position of the attached electromagnetic sensors on the tongue, lips, and jaw. EMA may provide either a 2D or 3D landmark localization in milliseconds, but the system operation is complex and uncomfortable to be used in all cases on a daily basis; it might be more usable for conducting clinical studies at research centres.…”

Section: Introductionmentioning

confidence: 99%

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

et al. 2022

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Lingual ultrasound imaging is essential in linguistic research and speech recognition. It has been used widely in different applications as visual feedback to enhance language learning for non-native speakers, study speech-related disorders and remediation, articulation research and analysis, swallowing study, tongue 3D modelling, and silent speech interface. This article provides a comparative analysis and review based on quantitative and qualitative criteria of the two main streams of tongue contour segmentation from ultrasound images. The first stream utilizes traditional computer vision and image processing algorithms for tongue segmentation. The second stream uses machine and deep learning algorithms for tongue segmentation. The results show that tongue tracking using machine learning-based techniques is superior to traditional techniques, considering the performance and algorithm generalization ability. Meanwhile, traditional techniques are helpful for implementing interactive image segmentation to extract valuable features during training and postprocessing. We recommend using a hybrid approach to combine machine learning and traditional techniques to implement a real-time tongue segmentation tool.

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