# An open-source voice type classifier for child-centered daylong recordings

arXiv: https://arxiv.org/abs/2005.12656 · HTML: https://mlsanity.com/paper/2005.12656/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 14 of 14)

- [BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models](https://mlsanity.com/paper/2306.01506/) (2023)
- [The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences](https://mlsanity.com/paper/2406.10447/) (2024)
- [BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings](https://mlsanity.com/paper/2509.15001/) (2025)
- [Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity](https://mlsanity.com/paper/2506.11075/) (2025)
- [Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier](https://mlsanity.com/paper/2506.11074/) (2025)
- [Assessing the alignment between infants' visual and linguistic experience using multimodal language models](https://mlsanity.com/paper/2511.18824/) (2025)
- [Is the Language Familiarity Effect gradual? A computational modelling approach](https://mlsanity.com/paper/2206.13415/) (2022)
- [Employing self-supervised learning models for cross-linguistic child speech maturity classification](https://mlsanity.com/paper/2506.08999/) (2025)
- [Speech Detection For Child-Clinician Conversations In Danish For Low-Resource In-The-Wild Conditions: A Case Study](https://mlsanity.com/paper/2204.11550/) (2022)
- [Who Said What WSW 2.0? Enhanced Automated Analysis of Preschool Classroom Speech](https://mlsanity.com/paper/2505.09972/) (2025)
- [Enabling automatic transcription of child-centered audio recordings from real-world environments](https://mlsanity.com/paper/2506.11747/) (2025)
- [Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?](https://mlsanity.com/paper/2508.21210/) (2025)
- [Context-aware child-directed speech detection from long-form recordings](https://mlsanity.com/paper/2606.01134/) (2026)
- [Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning](https://mlsanity.com/paper/2608.11587/) (2026)

## Abstract (arXiv)

Spontaneous conversations in real-world settings such as those found in child-centered recordings have been shown to be amongst the most challenging audio files to process. Nevertheless, building speech processing models handling such a wide variety of conditions would be particularly useful for language acquisition studies in which researchers are interested in the quantity and quality of the speech that children hear and produce, as well as for early diagnosis and measuring effects of remediation. In this paper, we present our approach to designing an open-source neural network to classify audio segments into vocalizations produced by the child wearing the recording device, vocalizations produced by other children, adult male speech, and adult female speech. To this end, we gathered diverse child-centered corpora which sums up to a total of 260 hours of recordings and covers 10 languages. Our model can be used as input for downstream tasks such as estimating the number of words produced by adult speakers, or the number of linguistic units produced by children. Our architecture combines SincNet filters with a stack of recurrent layers and outperforms by a large margin the state-of-the-art system, the Language ENvironment Analysis (LENA) that has been used in numerous child language studies.