The Dorsal Differentiation of Velar From Alveolar Stops in Typically Developing Children and Children With Persistent Velar Fronting

Cleland, Joanne; Scobbie, James M.

doi:10.1044/2020_jslhr-20-00373

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…Future research on the level of articulatory variability in the speech of typically-developing children is needed, but based on these two small studies it appears that the 'additional' variability and timing errors identified by the ultrasound-aided transcribers in the present study most likely represent errors that may be indicative of subtle motor speech difficulties (Vick et al, 2014). Indeed, previous research has identified errors or differences in tongue shape and/or movement between productions in this population (Cleland & Scobbie, 2021). This point will be expanded upon below.…”

Section: Error Detection and Transcription Reliabilitymentioning

confidence: 74%

Using ultrasound tongue imaging to support the phonetic transcription of childhood speech sound disorders

Sugden

Cleland

2021

Clinical Linguistics & Phonetics

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This study aims to determine whether adding an additional modality (ultrasound tongue imaging) improves the inter-rater reliability of phonetic transcription in childhood speech sound disorders (SSDs) and whether it enables the identification of different or additional errors in children's speech. Twenty-three English speaking children aged 5-13 years with SSDs of unknown origin were recorded producing repetitions of /aCa/ for all places of articulation with simultaneous audio and ultrasound. Two types of transcriptions were undertaken off-line: (1) ultrasound-aided transcription by two ultrasound-trained speech-language pathologists (SLPs) and (2) traditional phonetic transcription from audio recordings, completed by the same two SLPs and additionally by two different SSD specialist SLPs. We classified transcriptions and errors into ten different subcategories and compared: the number of consonants identified as in error by each transcriber; the inter-rater reliability; and the relative frequencies of error types identified by the different types of transcriber. Results showed that error-detection rates were different across the transcription types, with the ultrasound-aided transcribers identifying more errors than were identified using traditional audio-only transcription. Analysis revealed that these additional errors were identified on the dynamic ultrasound image despite being transcribed as correct, suggestive of subtle motor speech differences. Interrater reliability for classifying the type of error was substantial (κ = 0.72) for the ultrasound-aided transcribers and ranged from fair to moderate for the audio-only transcribers (κ = 0.38 to 0.52). Ultrasound-aided transcribers identified more instances of increased variability and abnormal timing errors than the audio-only transcribers.

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Section: Error Detection and Transcription Reliabilitymentioning

confidence: 74%

Using ultrasound tongue imaging to support the phonetic transcription of childhood speech sound disorders

Sugden

Cleland

2021

Clinical Linguistics & Phonetics

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“…It was measured using the less conservative ZoS, one comprising no fewer than five contiguous significantly different radii, augmented both by adjacent crossovers and by further contiguous radii on which the alveolar and velar targets were also significantly different. Unlike the KTMax measure reported in Cleland. and Scobbie (2021), this measure does not capture just the size of a canonical dorsal crescent between the two tongue curves, but is more global.…”

Section: Mean Radial Difference (Mrd)mentioning

confidence: 93%

“…The MRD is therefore a suitable measure for comparing a wider range of significantly different tongue shape configurations (rather than only the depth of a canonically-shaped alveolar vs. velar contrast near the velum). Because it can measure both dorsal and tongue root differences and incorporates the cross-over between these areas in which the two tongue splines are the same distance from the origin of measurement fan, the MRD measure, from data presented in this paper is not directly comparable to KTMax (Cleland. & Scobbie, 2021).…”

Section: Mean Radial Difference (Mrd)mentioning

confidence: 99%

“…Quantitative measures are therefore crucial in the analysis of ultrasound data, particularly that of any clinical population, to identify any covert errors or covert contrasts, such as those found by Cleland et al (2017) in speakers with speech disorders of unknown origin. Cleland. and Scobbie (2021) provided norms from 30 typically developing children with which to compare potentially incorrect /t/ and /k/ productions.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying changes in ultrasound tongue-shape pre- and post-intervention in speakers with submucous cleft palate: an illustrative case study

Roxburgh

Cleland

Scobbie

et al. 2021

Clinical Linguistics & Phonetics

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Ultrasound Tongue Imaging is increasingly used during assessment and treatment of speech sound disorders. Recent literature has shown that ultrasound is also useful for the quantitative analysis of a wide range of speech errors. So far, the compensatory articulations of speakers with cleft palate have only been analysed qualitatively. This study provides a pilot quantitative ultrasound analysis, drawing on longitudinal intervention data from a child with submucous cleft palate. Two key ultrasound metrics were used: 1. articulatory t-tests were used to compare tongue-shapes for perceptually collapsed phonemes on a radial measurement grid and 2. the Mean Radial Difference was reported to quantify the extent to which the two tongue shapes differ, overall. This articulatory analysis supplemented impressionistic phonetic transcriptions and identified covert contrasts. Articulatory errors identified in this study using ultrasound were in line with errors identified in the speech of children with cleft palate in previous literature. While compensatory error patterns commonly found in speakers with cleft palate have been argued to facilitate functional phonological development, the nature of our findings suggest that the compensatory articulations uncovered are articulatory in nature.

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“…In speech and language therapy, ultrasound tongue imaging can be used to diagnose a range of speech dif-ficulties, and to provide visual biofeedback in therapy for different types of speech sound disorders, including those arising from a cleft lip or palate (Sugden et al, 2019;Roxburgh et al, 2015;Cleland et al, 2020). During intervention, ultrasound can be used as an objective measure of the patient's progress (Cleland & Scobbie, 2021), or to complement verbal feedback and contribute to positive reinforcement (Roxburgh et al, 2015). Ultrasound also assists annotators in identifying covert articulation errors (Cleland et al, 2017) and has been shown to increase inter-annotator agreement when transcribing the speech of children with cleft lip and palate (Cleland et al, 2020).…”

Section: Ultrasound Tongue Imagingmentioning

confidence: 99%

Automatic audiovisual synchronisation for ultrasound tongue imaging

Eshky,

Cleland,

Ribeiro

et al. 2021

Preprint

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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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The Dorsal Differentiation of Velar From Alveolar Stops in Typically Developing Children and Children With Persistent Velar Fronting

Cited by 12 publications

References 28 publications

Using ultrasound tongue imaging to support the phonetic transcription of childhood speech sound disorders

Using ultrasound tongue imaging to support the phonetic transcription of childhood speech sound disorders

Quantifying changes in ultrasound tongue-shape pre- and post-intervention in speakers with submucous cleft palate: an illustrative case study

Automatic audiovisual synchronisation for ultrasound tongue imaging

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