An automated assessment framework for atypical prosody and stereotyped idiosyncratic phrases related to autism spectrum disorder

Li, Ming; Tang, Dengke; Zeng, Junlin; Zhou, Tianyan; Zhu, Huilin; Chen, Biyuan; Zou, Xiaobing

doi:10.1016/j.csl.2018.11.002

Cited by 43 publications

(26 citation statements)

References 18 publications

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“…Even so, it should be noted that further research should compile a bigger and more balanced corpus of the speech of individuals with Down syndrome and should also record a reference corpus of people with typical development. A bigger corpus will have to be compiled in order to explore new approaches such as end-to-end deep learning methods, which have shown promising results in the assessment of atypical prosody in other populations with intellectual disabilities, such as Autism Spectrum Disorder [47].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Automatic Assessment of Prosodic Quality in Down Syndrome: Analysis of the Impact of Speaker Heterogeneity

et al. 2019

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Prosody is a fundamental speech element responsible for communicative functions such as intonation, accent and phrasing, and prosodic impairments of individuals with intellectual disabilities reduce their communication skills. Yet, technological resources have paid little attention to prosody. This study aims to develop an automatic classifier to predict the prosodic quality of utterances produced by individuals with Down syndrome, and to analyse how inter-individual heterogeneity affects assessment results. A therapist and an expert in prosody judged the prosodic appropriateness of a corpus of Down syndrome' utterances collected through a video game. The judgments of the expert were used to train an automatic classifier that predicts prosodic quality by using a set of fundamental frequency, duration and intensity features. The classifier accuracy was 79.3% and its true positive rate 89.9%. We analyzed how informative each of the features was for the assessment and studied relationships between participants' developmental level and results: interspeaker variability conditioned the relative weight of prosodic features for automatic classification and participants' developmental level was related to the prosodic quality of their productions. Therefore, since speaker variability is an intrinsic feature of individuals with Down syndrome, it should be considered to attain an effective automatic prosodic assessment system.

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Section: Limitations and Future Workmentioning

confidence: 99%

Automatic Assessment of Prosodic Quality in Down Syndrome: Analysis of the Impact of Speaker Heterogeneity

et al. 2019

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“…Ming Li et al [11] proposed end-to-end deep learning methods achieve superior performance at the segment level, but not at the person-level. Deep learning methods for getting the best improvement in performance at segment level.…”

Section: Types Of Asr Systemsmentioning

confidence: 99%

Automatic Speech Recognition Systems for Regional Languages in India

2019

ijrte

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Speech recognition systems has made remarkable progress in last ¬few decades such as Siri, Google assistant, Cortana. For improving the automation in services of all sectors including medical, agriculture, voice dialling, directory services, education, automobile etc., ASR systems must be built for regional languages as most of the Indian population in not familiar with English. Lots of work is done for English language but not for regional languages in India. Developing ASR and ASU systems will change the scenario of current service sector. There are many challenges in building ASR system, Noise reduction is a one of the challenging and still unsolved parameters which affects a lot on performance of any ASR system. Basically, three models required for building any ASR systems- Language model, acoustic model and pronunciation model. In this paper, discussed various parameters affecting on building ASR systems, development of ASR systems, Tools and Techniques used for building an ASR system and research on regional languages ASR system. Deep Neural network (DNN) provides a better way of recognising a speech and accuracy is high.

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“…Such automated assessments can help clinicians objectively quantify skills in this domain of functioning for which no standardised, agenormed, validated measures are available. [17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Interplay Between Affective, Phonatory and Motoric Subsystems in Autism Spectrum Disorder Using a Multimodal Dialogue Agent

Kothare

Ramanarayanan

Roesler³

et al. 2021

Preprint

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We explore the utility of an on-demand multimodal conversational platform in extracting speech and facial metrics in children with Autism Spectrum Disorder (ASD). We investigate the extent to which these metrics correlate with objective clinical measures, particularly as they pertain to the interplay between the affective, phonatory and motoric subsystems. 22 participants diagnosed with ASD engaged with a virtual agent in conversational affect production tasks designed to elicit facial and vocal affect. We found significant correlations between vocal pitch and loudness extracted by our platform during these tasks and accuracy in recognition of facial and vocal affect, assessed via the Diagnostic Analysis of Nonverbal Accuracy-2 (DANVA-2) neuropsychological task. We also found significant correlations between jaw kinematic metrics extracted using our platform and motor speed of the dominant hand assessed via a standardised neuropsychological finger tapping task. These findings offer preliminary evidence for the usefulness of these audiovisual analytic metrics and could help us better model the interplay between different physiological subsystems in individuals with ASD.

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An automated assessment framework for atypical prosody and stereotyped idiosyncratic phrases related to autism spectrum disorder

Cited by 43 publications

References 18 publications

Automatic Assessment of Prosodic Quality in Down Syndrome: Analysis of the Impact of Speaker Heterogeneity

Automatic Assessment of Prosodic Quality in Down Syndrome: Analysis of the Impact of Speaker Heterogeneity

Automatic Speech Recognition Systems for Regional Languages in India

Investigating the Interplay Between Affective, Phonatory and Motoric Subsystems in Autism Spectrum Disorder Using a Multimodal Dialogue Agent

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