Vowel variability and contrast in Childhood Apraxia of Speech: acoustics and articulation

Lenoci, Giovanna; Celata, Chiara; Ricci, Irene; Chilosi, Alberto; Barone, Vincenzo

doi:10.1080/02699206.2020.1853811

Cited by 8 publications

(4 citation statements)

References 68 publications

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“…To determine whether increased rate leads to greater articulatory instability we compared the standard deviation of the tongue splines of the slowest rate productions to the fastest rate productions, by syllable, bi-syllable or trisyllable, using Bayes independent sample test between groups. Similar studies, for example, Lenoci et al (2020) and Zharkova and Hewlett (2009) also report standard deviations for token to token variability (though at normal speaking rate), but in both these studies the nearest-neighbour method is used, which is based on Euclidian distances within a Cartesian co-ordinate system. Again, we choose to use a polar co-ordinate system to avoid errors where the tongue is not parallel to the horizontal axis.…”

Section: Ultrasound Statistical Analysesmentioning

confidence: 99%

Using ultrasound tongue imaging to analyse maximum performance tasks in children with Autism: a pilot study

McKeever

Cleland

Delafield‐Butt

2021

Clinical Linguistics & Phonetics

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This study proposes a protocol for assessing speech motor control in children using maximum performance tasks with simultaneous acoustic and ultrasound recording. The protocol was piloted on eight children with autism spectrum disorders and nine typically developing children. Diadochokinesis rate, accuracy, and consistency were elicited using an imitation paradigm where speakers repeat mono-, bi-, and trisyllabic stimuli at increasing rates. Both traditional measures of rate, accuracy and consistency and an ultrasound tongue-shape analysis of slow versus fast productions were undertaken. Preliminary results suggest that the protocol is feasible with children with communication disorders. Instrumental measures suggest greater variability in tongue movements in the children with autism that is not detected using perceptual measures of accuracy. A subgroup of children with autism showed some evidence of differences in speech motor control. Ultrasound tongue imaging appears to be a useful method for gaining additional insight into speech motor control.

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Section: Ultrasound Statistical Analysesmentioning

confidence: 99%

Using ultrasound tongue imaging to analyse maximum performance tasks in children with Autism: a pilot study

McKeever

Cleland

Delafield‐Butt

2021

Clinical Linguistics & Phonetics

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“…for example, Suppa et al [28] performed acoustical analysis of a sustained /e/ to detect Parkinson's disease in elderly patients; however, these utterances are more sensitive to dialects, and therefore, results may be less reliable. Instead, /a/, /I/, and /u/, usually referred to as cardinal or corner vowels, are characterized by a well-defined vocal tract configuration and remain stable during articulation, which makes them substantially independent of dialectal and even linguistic diversity [29].…”

Section: Vocal Tasksmentioning

confidence: 99%

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics

Calà,

Frassineti,

Sforza

et al. 2023

Bioengineering

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Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith–Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal–Wallis test and post hoc analysis with Dunn–Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.

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“…Vowels /a/, /i/, and /u/ represent Italian cardinal vowels as they are characterized by well-defined vocal tract configurations which make their phonation quite stable regardless of dialectal inflections. 26 The recording of each sound was repeated three times for each subject at conversational tone and intensity in the following 7 different PPE configurations, for a total of 210 voice signals:…”

Section: A Recordingsmentioning

confidence: 99%

Speaking with mask in the COVID-19 era: Multiclass machine learning classification of acoustic and perceptual parameters

Calà

Manfredi

Battilocchi

et al. 2023

The Journal of the Acoustical Society of America

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The intensive use of personal protective equipment often requires increasing voice intensity, with possible development of voice disorders. This paper exploits machine learning approaches to investigate the impact of different types of masks on sustained vowels /a/, /i/, and /u/ and the sequence /a'jw/ inside a standardized sentence. Both objective acoustical parameters and subjective ratings were used for statistical analysis, multiple comparisons, and in multivariate machine learning classification experiments. Significant differences were found between mask+shield configuration and no-mask and between mask and mask+shield conditions. Power spectral density decreases with statistical significance above 1.5 kHz when wearing masks. Subjective ratings confirmed increasing discomfort from no-mask condition to protective masks and shield. Machine learning techniques proved that masks alter voice production: in a multiclass experiment, random forest (RF) models were able to distinguish amongst seven masks conditions with up to 94% validation accuracy, separating masked from unmasked conditions with up to 100% validation accuracy and detecting the shield presence with up to 86% validation accuracy. Moreover, an RF classifier allowed distinguishing male from female subject in masked conditions with 100% validation accuracy. Combining acoustic and perceptual analysis represents a robust approach to characterize masks configurations and quantify the corresponding level of discomfort.

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Vowel variability and contrast in Childhood Apraxia of Speech: acoustics and articulation

Cited by 8 publications

References 68 publications

Using ultrasound tongue imaging to analyse maximum performance tasks in children with Autism: a pilot study

Using ultrasound tongue imaging to analyse maximum performance tasks in children with Autism: a pilot study

Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics

Speaking with mask in the COVID-19 era: Multiclass machine learning classification of acoustic and perceptual parameters

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