Year: 2026
Type: conference-paper
Abstract: Language identification (LID) systems are widely used in speech analytics, multilingual routing, and spoken-language interfaces, but their performance often drops in real-world conditions because of domain mismatch, including background noise, reverberation, channel effects, codecs, and far-field recording.We propose an embedding-level generative enhancement method based on flow matching that refines language embeddings from a pre-trained LID model.During training, we build paired embeddings from clean utterances and their distorted versions, and learn a continuous-time transformation that maps corrupted embeddings toward clean ones while preserving language-discriminative structure.At inference, any embedding is passed through the learned flow to obtain a refined, more robust representation.The method requires no changes to the LID pipeline and no language labels, while improving LID accuracy under challenging conditions without hurting standard performance.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7167679779