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CUCHILD: A Large-Scale Cantonese Corpus of Child Speech for Phonology and Articulation Assessment
This paper describes the design and development of CUCHILD, a large-scal...
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The SLT 2021 children speech recognition challenge: Open datasets, rules and baselines
Automatic speech recognition (ASR) has been significantly advanced with ...
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Dyslexia and Dysgraphia prediction: A new machine learning approach
Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfe...
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Automatic Detection of Phonological Errors in Child Speech Using Siamese Recurrent Autoencoder
Speech sound disorder (SSD) refers to the developmental disorder in whic...
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Relatives in the same university faculty: nepotism or merit?
In many countries culture, practice or regulations inhibit the co-presen...
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EEG source localization analysis in epileptic children during a visual working-memory task
We localize the sources of brain activity of children with epilepsy base...
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God(s) Know(s): Developmental and Cross-Cultural Patterns in Children Drawings
This paper introduces a novel approach to data analysis designed for the...
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Analysis of Disfluency in Children's Speech
Disfluencies are prevalent in spontaneous speech, as shown in many studies of adult speech. Less is understood about children's speech, especially in pre-school children who are still developing their language skills. We present a novel dataset with annotated disfluencies of spontaneous explanations from 26 children (ages 5–8), interviewed twice over a year-long period. Our preliminary analysis reveals significant differences between children's speech in our corpus and adult spontaneous speech from two corpora (Switchboard and CallHome). Children have higher disfluency and filler rates, tend to use nasal filled pauses more frequently, and on average exhibit longer reparandums than repairs, in contrast to adult speakers. Despite the differences, an automatic disfluency detection system trained on adult (Switchboard) speech transcripts performs reasonably well on children's speech, achieving an F1 score that is 10% higher than the score on an adult out-of-domain dataset (CallHome).
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