# Causal Structure Discovery for Error Diagnostics of Children's ASR

arXiv: https://arxiv.org/abs/2506.00402 · HTML: https://mlsanity.com/paper/2506.00402/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 18 of 18)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [Causal-learn: Causal Discovery in Python](https://mlsanity.com/paper/2307.16405/) (2023)
- [Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Models](https://mlsanity.com/paper/2406.10507/) (2024)
- [My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speech](https://mlsanity.com/paper/2309.13347/) (2023)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR](https://mlsanity.com/paper/2305.00115/) (2023)
- [End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study](https://mlsanity.com/paper/2102.09918/) (2021)
- [A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition](https://mlsanity.com/paper/2204.05419/) (2022)
- [Learning Post-Hoc Causal Explanations for Recommendation](https://mlsanity.com/paper/2006.16977/) (2020)
- [Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition](https://mlsanity.com/paper/2102.07259/) (2021)
- [Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech](https://mlsanity.com/paper/2203.06600/) (2022)
- [ChildAugment: Data Augmentation Methods for Zero-Resource Children's Speaker Verification](https://mlsanity.com/paper/2402.15214/) (2024)
- [Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors](https://mlsanity.com/paper/2502.08587/) (2025)
- [Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks](https://mlsanity.com/paper/2312.14333/) (2023)

## Abstract (arXiv)

Children's automatic speech recognition (ASR) often underperforms compared to that of adults due to a confluence of interdependent factors: physiological (e.g., smaller vocal tracts), cognitive (e.g., underdeveloped pronunciation), and extrinsic (e.g., vocabulary limitations, background noise). Existing analysis methods examine the impact of these factors in isolation, neglecting interdependencies-such as age affecting ASR accuracy both directly and indirectly via pronunciation skills. In this paper, we introduce a causal structure discovery to unravel these interdependent relationships among physiology, cognition, extrinsic factors, and ASR errors. Then, we employ causal quantification to measure each factor's impact on children's ASR. We extend the analysis to fine-tuned models to identify which factors are mitigated by fine-tuning and which remain largely unaffected. Experiments on Whisper and Wav2Vec2.0 demonstrate the generalizability of our findings across different ASR systems.