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Protocol for SonoSpeech Cleft Pilot: a mixed-methods pilot randomized control trial of ultrasound visual biofeedback versus standard intervention for children with cleft lip and palate
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In: Pilot Feasibility Stud (2022)
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| 2 |
Protocol for SonoSpeech Cleft Pilot : a mixed-methods pilot randomised control trial of ultrasound visual biofeedback versus standard intervention for children with cleft lip and palate
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SonoSpeech cleft pilot : a pilot randomised control trial of ultrasound visual biofeedback versus standard intervention for children with cleft lip and palate
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Automatic audiovisual synchronisation for ultrasound tongue imaging ...
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KT Dorsal Crescents & polar coordinates, using AAA v2.18.03 ...
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KT Dorsal Crescents & polar coordinates, using AAA v2.18.03 ...
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| 7 |
Quantifying changes in ultrasound tongue-shape pre- and post-intervention in speakers with submucous cleft palate: An illustrative case study
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The dorsal differentiation of velar from alveolar stops in typically developing children and children with persistent velar fronting
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The dorsal differentiation of velar from alveolar stops in typically developing children and children with persistent velar fronting
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Automatic audiovisual synchronisation for ultrasound tongue imaging
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Exploiting ultrasound tongue imaging for the automatic detection of speech articulation errors
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Using ultrasound tongue imaging to analyse maximum performance tasks in children with autism : a pilot study
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| 14 |
Quantifying changes in ultrasound tongue-shape pre- and post-intervention in speakers with submucous cleft palate : an illustrative case study
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| 15 |
Using ultrasound tongue imaging to support the phonetic transcription of childhood speech sound disorders
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Automatic audiovisual synchronisation for ultrasound tongue imaging
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Abstract:
Ultrasound tongue imaging is used to visualise the intra-oral articulators during speech production. It is utilised in a range of applications, including speech and language therapy and phonetics research. Ultrasound and speech audio are recorded simultaneously, and in order to correctly use this data, the two modalities should be correctly synchronised. Synchronisation is achieved using specialised hardware at recording time, but this approach can fail in practice resulting in data of limited usability. In this paper, we address the problem of automatically synchronising ultrasound and audio after data collection. We first investigate the tolerance of expert ultrasound users to synchronisation errors in order to find the thresholds for error detection. We use these thresholds to define accuracy scoring boundaries for evaluating our system. We then describe our approach for automatic synchronisation, which is driven by a self-supervised neural network, exploiting the correlation between the two signals to synchronise them. We train our model on data from multiple domains with different speaker characteristics, different equipment, and different recording environments, and achieve an accuracy >92.4% on held-out in-domain data. Finally, we introduce a novel resource, the Cleft dataset, which we gathered with a new clinical subgroup and for which hardware synchronisation proved unreliable. We apply our model to this out-of-domain data, and evaluate its performance subjectively with expert users. Results show that users prefer our model's output over the original hardware output 79.3% of the time. Our results demonstrate the strength of our approach and its ability to generalise to data from new domains.
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Keyword:
461104 - Neural networks
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URL: https://hdl.handle.net/1959.7/uws:62528
https://doi.org/10.1016/j.specom.2021.05.008
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| 17 |
[In Press] Using ultrasound tongue imaging to support the phonetic transcription of childhood speech sound disorders
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The impact of real-time articulatory information on phonetic transcription : ultrasound-aided transcription in cleft lip and palate speech
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