Quantitative analysis of pitch in speech of children with neurodevelopmental disorders

Kiss, Gábor; Santen, Jan P. H. van; Prud’hommeaux, Emily; Black, Lois M.

doi:10.21437/interspeech.2012-322

Cited by 13 publications

(3 citation statements)

References 12 publications

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“…Therefore, features with remarkable contributions to distinguish ASD from TD groups were selected with tools like correlation analysis [53,67], principal component analysis, factor analysis [18,62], ElasticNet [63][64][65], and Geneva Minimalistic Acoustic Parameter Set (GeMAPS) [68]. Data selection was further classified by tools such as native Bayed (NB) [42], support vector machines (SVMs) [5,20,41,60,66,68], probabilistic neural networks (PNNs) [19], speech-related vocal islands (SVIs) [62], or random forests [67]. Since machine learning was not merely to find a model explaining the current data but to create a model that generalizing to new data [69].…”

Section: Results From Machine Learning For Asd Diagnosismentioning

confidence: 99%

“…Since machine learning was not merely to find a model explaining the current data but to create a model that generalizing to new data [69]. To ensure generation for out-of-data testing, cross-validation (CV) [66,67] was frequently reported, with 5-fold CV [32,[63][64][65][66], 10-fold CV [5,19], and leave-out procedures [19,39,42,67]. For a more comprehensive introduction and overview of multivariate machine learning processes, please see books written by Bishop [70] and Hastie et al [71].…”

Section: Results From Machine Learning For Asd Diagnosismentioning

confidence: 99%

“…However, despite the promising accuracy of ASD detection using machine learning techniques, the inclusion of various acoustic parameters in different algorithms has led to remarkable variations in extant observations [39]. For instance, the algorithms trained by rhythm or by intonation relevant characteristics reached significantly different Area Under Curves (AUCs; e.g., 88.6%, 75%, and 55.9%) [5,42]. Therefore, these aforementioned findings have highlighted the promising prospects as well as the heterogeneity of machine learning in ASD detection.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Ma,

Xu,

Zhang

et al. 2024

Behavioral Sciences

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Natural speech plays a pivotal role in communication and interactions between human beings. The prosody of natural speech, due to its high ecological validity and sensitivity, has been acoustically analyzed and more recently utilized in machine learning to identify individuals with autism spectrum disorders (ASDs). In this meta-analysis, we evaluated the findings of empirical studies on acoustic analysis and machine learning techniques to provide statistically supporting evidence for adopting natural speech prosody for ASD detection. Using a random-effects model, the results observed moderate-to-large pooled effect sizes for pitch-related parameters in distinguishing individuals with ASD from their typically developing (TD) counterparts. Specifically, the standardized mean difference (SMD) values for pitch mean, pitch range, pitch standard deviation, and pitch variability were 0.3528, 0.6744, 0.5735, and 0.5137, respectively. However, the differences between the two groups in temporal features could be unreliable, as the SMD values for duration and speech rate were only 0.0738 and −0.0547. Moderator analysis indicated task types were unlikely to influence the final results, whereas age groups showed a moderating role in pooling pitch range differences. Furthermore, promising accuracy rates on ASD identification were shown in our analysis of multivariate machine learning studies, indicating averaged sensitivity and specificity of 75.51% and 80.31%, respectively. In conclusion, these findings shed light on the efficacy of natural prosody in identifying ASD and offer insights for future investigations in this line of research.

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Section: Results From Machine Learning For Asd Diagnosismentioning

confidence: 99%

Section: Results From Machine Learning For Asd Diagnosismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Ma,

Xu,

Zhang

et al. 2024

Behavioral Sciences

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Combining voice and language features improves automated autism detection

et al. 2022

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Variability in expressive and receptive language, difficulty with pragmatic language, and prosodic difficulties are all features of autism spectrum disorder (ASD). Quantifying language and voice characteristics is an important step for measuring outcomes for autistic people, yet clinical measurement is cumbersome and costly. Using natural language processing (NLP) methods and a harmonic model of speech, we analyzed language transcripts and audio recordings to automatically classify individuals as ASD or non‐ASD. One‐hundred fifty‐eight participants (88 ASD, 70 non‐ASD) ages 7 to 17 were evaluated with the autism diagnostic observation schedule (ADOS‐2), module 3. The ADOS‐2 was transcribed following modified SALT guidelines. Seven automated language measures (ALMs) and 10 automated voice measures (AVMs) for each participant were generated from the transcripts and audio of one ADOS‐2 task. The measures were analyzed using support vector machine (SVM; a binary classifier) and receiver operating characteristic (ROC). The AVM model resulted in an ROC area under the curve (AUC) of 0.7800, the ALM model an AUC of 0.8748, and the combined model a significantly improved AUC of 0.9205. The ALM model better detected ASD participants who were younger and had lower language skills and shorter activity time. ASD participants detected by the AVM model had better language profiles than those detected by the language model. In combination, automated measurement of language and voice characteristics successfully differentiated children with and without autism. This methodology could help design robust outcome measures for future research. Lay Summary People with autism often struggle with communication differences which traditional clinical measures and language tests cannot fully capture. Using language transcripts and audio recordings from 158 children ages 7 to 17, we showed that automated, objective language and voice measurements successfully predict the child's diagnosis. This methodology could help design improved outcome measures for research.

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System Assisted Vocal Response Analysis and Assessment of Autism in Children: A Machine Learning Based Approach

Khan,

Basu,

Basu

et al. 2023

Lecture Notes in Computer Science

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Quantitative analysis of pitch in speech of children with neurodevelopmental disorders

Cited by 13 publications

References 12 publications

Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis

Combining voice and language features improves automated autism detection

System Assisted Vocal Response Analysis and Assessment of Autism in Children: A Machine Learning Based Approach

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