Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.02259 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918134324133888 |
|---|---|
| author | Bonafos, Guillem Rouch, Jéremy Lego, Lény Reby, David Patural, Hugues Mathevon, Nicolas Emonet, Rémy |
| author_facet | Bonafos, Guillem Rouch, Jéremy Lego, Lény Reby, David Patural, Hugues Mathevon, Nicolas Emonet, Rémy |
| contents | Transfer learning using latent representations from pre-trained speech models achieves outstanding performance in tasks where labeled data is scarce. However, their applicability to non-speech data and the specific acoustic properties encoded in these representations remain largely unexplored. In this study, we investigate both aspects. We evaluate five pre-trained speech models on eight baby cries datasets, encompassing 115 hours of audio from 960 babies. For each dataset, we assess the latent representations of each model across all available classification tasks. Our results demonstrate that the latent representations of these models can effectively classify human baby cries and encode key information related to vocal source instability and identity of the crying baby. In addition, a comparison of the architectures and training strategies of these models offers valuable insights for the design of future models tailored to similar tasks, such as emotion detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02259 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Speech transformer models for extracting information from baby cries Bonafos, Guillem Rouch, Jéremy Lego, Lény Reby, David Patural, Hugues Mathevon, Nicolas Emonet, Rémy Sound Machine Learning Applications Transfer learning using latent representations from pre-trained speech models achieves outstanding performance in tasks where labeled data is scarce. However, their applicability to non-speech data and the specific acoustic properties encoded in these representations remain largely unexplored. In this study, we investigate both aspects. We evaluate five pre-trained speech models on eight baby cries datasets, encompassing 115 hours of audio from 960 babies. For each dataset, we assess the latent representations of each model across all available classification tasks. Our results demonstrate that the latent representations of these models can effectively classify human baby cries and encode key information related to vocal source instability and identity of the crying baby. In addition, a comparison of the architectures and training strategies of these models offers valuable insights for the design of future models tailored to similar tasks, such as emotion detection. |
| title | Speech transformer models for extracting information from baby cries |
| topic | Sound Machine Learning Applications |
| url | https://arxiv.org/abs/2509.02259 |