# Analysis of Disfluency in Children's Speech

arXiv: https://arxiv.org/abs/2010.04293 · HTML: https://mlsanity.com/paper/2010.04293/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Children's Voice Privacy: First Steps And Emerging Challenges](https://mlsanity.com/paper/2506.00100/) (2025)
- [Children's Speech Recognition through Discrete Token Enhancement](https://mlsanity.com/paper/2406.13431/) (2024)
- [K-Function: Joint Pronunciation Transcription and Feedback for Evaluating Kids Language Function](https://mlsanity.com/paper/2507.03043/) (2025)
- [Beyond Length: Context-Aware Expansion and Independence as Developmentally Sensitive Evaluation in Child Utterances](https://mlsanity.com/paper/2602.05392/) (2026)
- [How Well Do Self-Supervised Speech Models Encode Age and Gender in Children's Speech? A Layer-Wise Analysis Across Multiple Architectures](https://mlsanity.com/paper/2606.22177/) (2026)
- [Deep Learning Techniques for Phoneme Recognition in Italian Children' s Speech](https://mlsanity.com/paper/2609.33060/) (2026)

## Abstract (arXiv)

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).