Year: 2025
Type: conference-paper
Abstract: 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.
Institutions: Laboratoire Hubert Curien, Centre de Recherche en Neurosciences de Lyon, Centre Hospitalier Le Vinatier, Institut Universitaire de France, Centre Hospitalier Universitaire de Saint-Étienne, SAnté INgenierie BIOlogie Saint-Etienne, Université Jean Monnet, Université Jean Monnet, Centre Inria de Lyon, MALICE: Apprentissage automatique avec intégration des connaissances en ingénierie de surface : théorie et algorithmes
Language: English
Subfield: Pharmacy
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4414713040
FWCI:
Cites:
Cited by:
Related to: