Enhancing Low-Resource Spoken Language Identification Via Cross-Modality Retrieval and Cross-Lingual Text-to-Speech Synthesis

Résumé fourni par la source

Spoken language identification (SLID) for low-resource languages remains challenging due to limited data availability. In this paper, we present two novel approaches to address the issue: cross-modality retrieval-based data selection and cross-lingual text-to-speech (TTS) based data augmentation. Incorporating semi-supervised speech and synthetic speech produced by the two methods, we successfully enhance SLID on low-resource languages and on the full set of target languages, at a publicly available YouTube-derived dataset. Our best recipe reduces training data amount by 28% and ensures a more balanced distribution of training data across languages. The two general frameworks offer innovative strategies for leveraging resources to add valuable data to enhance SLID in extremely low-resource scenarios.