# Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults

arXiv: https://arxiv.org/abs/2309.07927 · HTML: https://mlsanity.com/paper/2309.07927/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers](https://mlsanity.com/paper/2307.03183/) (2023)
- [Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages](https://mlsanity.com/paper/2012.12121/) (2020)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition](https://mlsanity.com/paper/2306.02902/) (2023)
- [A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition](https://mlsanity.com/paper/2204.05419/) (2022)
- [Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition](https://mlsanity.com/paper/2102.09106/) (2021)

## Built on by (most cited first, 27 of 27)

- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments](https://mlsanity.com/paper/2409.14494/) (2024)
- [Selective Attention Merging for low resource tasks: A case study of Child ASR](https://mlsanity.com/paper/2501.08468/) (2025)
- [Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet](https://mlsanity.com/paper/2508.16576/) (2025)
- [Children's Speech Recognition through Discrete Token Enhancement](https://mlsanity.com/paper/2406.13431/) (2024)
- [SSVD: Structured SVD for Parameter-Efficient Fine-Tuning and Benchmarking under Domain Shift in ASR](https://mlsanity.com/paper/2509.02830/) (2025)
- [TICL+: A Case Study On Speech In-Context Learning for Children's Speech Recognition](https://mlsanity.com/paper/2512.18263/) (2025)
- [FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech Data](https://mlsanity.com/paper/2406.17926/) (2024)
- [Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges](https://mlsanity.com/paper/2410.11865/) (2024)
- [Mind the Shift: Using Delta SSL Embeddings to Enhance Child ASR](https://mlsanity.com/paper/2601.20142/) (2026)
- [SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning](https://mlsanity.com/paper/2506.02412/) (2025)
- [Evaluation of Speech Foundation Models for ASR on Child-Adult Conversations in Autism Diagnostic Sessions](https://mlsanity.com/paper/2409.16135/) (2024)
- [CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition](https://mlsanity.com/paper/2506.06071/) (2025)
- [SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research](https://mlsanity.com/paper/2506.09206/) (2025)
- [SSVD-O: Parameter-Efficient Fine-Tuning with Structured SVD for Speech Recognition](https://mlsanity.com/paper/2601.12600/) (2026)
- [Gumbel-BEARD: Automatic Layer Selection for Self-Supervised Adaptation of Whisper in Low-Resource Domains](https://mlsanity.com/paper/2606.11429/) (2026)
- [Examining Test-Time Adaptation for Personalized Child Speech Recognition](https://mlsanity.com/paper/2409.13095/) (2024)
- [From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data](https://mlsanity.com/paper/2505.17088/) (2025)
- [RealClass: A Framework for Classroom Speech Simulation with Public Datasets and Game Engines](https://mlsanity.com/paper/2510.01462/) (2025)
- [KidSpeak: A General Multi-purpose LLM for Kids' Speech Recognition and Screening](https://mlsanity.com/paper/2512.05994/) (2025)
- [InterventionLens: A Multi-Agent Framework for Detecting ASD Intervention Strategies in Parent-Child Shared Reading](https://mlsanity.com/paper/2603.13710/) (2026)
- [Abjad-Kids: An Arabic Speech Classification Dataset for Primary Education](https://mlsanity.com/paper/2603.20255/) (2026)
- [Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapy](https://mlsanity.com/paper/2607.27212/) (2026)
- [Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs](https://mlsanity.com/paper/2609.17981/) (2026)
- [AURA: Uncertainty-Routed Activation Editing for Acoustic Grounding in Speech Foundation Models](https://mlsanity.com/paper/2609.23979/) (2026)
- [Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs](https://mlsanity.com/paper/2609.38106/) (2026)
- [Child ASR Adaptation with Adult Retention: An Empirical Study](https://mlsanity.com/paper/2610.08827/) (2026)

## Abstract (arXiv)

Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data. However, this progress doesn't readily extend to ASR for children due to the limited availability of suitable child-specific databases and the distinct characteristics of children's speech. A recent study investigated leveraging the My Science Tutor (MyST) children's speech corpus to enhance Whisper's performance in recognizing children's speech. They were able to demonstrate some improvement on a limited testset. This paper builds on these findings by enhancing the utility of the MyST dataset through more efficient data preprocessing. We reduce the Word Error Rate (WER) on the MyST testset 13.93% to 9.11% with Whisper-Small and from 13.23% to 8.61% with Whisper-Medium and show that this improvement can be generalized to unseen datasets. We also highlight important challenges towards improving children's ASR performance. The results showcase the viable and efficient integration of Whisper for effective children's speech recognition.