Evaluation of Speech Foundation Models for ASR on Child-Adult Conversations in Autism Diagnostic Sessions
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Abstract (arXiv)
Reliable transcription of child-adult conversations in clinical settings is crucial for diagnosing developmental disorders like Autism. Recent advances in deep learning and availability of large scale transcribed data has led to development of speech foundation models that have shown dramatic improvements in ASR performance. However, their performance on conversational child-adult interactions remains underexplored. In this work, we provide a comprehensive evaluation of ASR performance on a dataset containing child-adult interactions from autism diagnostic sessions, using Whisper, Wav2Vec2, HuBERT, and WavLM. We find that speech foundation models show a noticeable performance drop (15-20% absolute WER) for child speech compared to adult speech in the conversational setting. Then, we fine-tune the best-performing zero-shot model (Whisper-large) using LoRA in a low-resource setting, yielding 8% and 13% absolute WER improvements for child and adult speech, respectively.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 24 Sep 2024.
Builds on
13 of 31 references.
References · 13
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2023Voicebox: Text-Guided Multilingual Universal Speech Generation at Scalecited by 379
- 2020Self-training and Pre-training are Complementary for Speech Recognitioncited by 99
- 2024Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation Modelscited by 29
- 2023Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adultscited by 27
- 2023Adaptation of Whisper models to child speech recognitioncited by 26
- 2023Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRcited by 23
- 2018Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendationscited by 22
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