ArticleInterspeech2023
Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio.
Article in Interspeech, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Bridging Machine Learning and Event-Based Analyses to Assess Maternal Contingent Responses to Infant Nondistress and Distress Vocalizations From Home Audio Recordings.Developmental science · 2026Article
- HMDF-Net: a transfer learning-based heterogeneous multimodal dynamic fusion network for depression detection among inmates in correctional facilities.Frontiers in psychiatry · 2026Article
- Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities.IEEE signal processing magazine · 2025Article
- ANALYSIS OF SELF-SUPERVISED SPEECH MODELS ON CHILDREN'S SPEECH AND INFANT VOCALIZATIONS.... IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops · 2024Article
- Evaluating Users' Experiences of a Child Multimodal Wearable Device: Mixed Methods Approach.JMIR human factors · 2024Article
- Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
To perform automatic family audio analysis, past studies have collected recordings using phone, video, or audio-only recording devices like LENA, investigated supervised learning methods, and used or fine-tuned general-purpose embeddings learned from large pretrained models. In this study, we advance the audio component of a new infant wearable multi-modal device called LittleBeats (LB) by learning family audio representation via wav2vec 2.0 (W2V2) pretraining. We show given a limited number of labeled LB home recordings, W2V2 pretrained using 1k-hour of unlabeled home recordings outperforms oracle W2V2 pretrained on 52k-hour unlabeled audio in terms of parent/infant speaker diarization (SD) and vocalization classifications (VC) at home. Extra relevant external unlabeled and labeled data further benefit W2V2 pretraining and fine-tuning. With SpecAug and environmental speech corruptions, we obtain 12% relative gain on SD and moderate boost on VC. Code and model weights are available.
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Registered trials
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