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Yanliang Li

3 accepted papers

2026

Bridging the Stability-Expressivity Gap: Synthetic Data Scaling and Preference Alignment for Low-Resource Spoken Language Models

ICML 2026poster

Spoken Language Models (SLMs) revolutionize speech synthesis by bypassing traditional linguistic front-ends, yet they remain limited by the digital resource disparities across languages. We investigate these challenges within the Southeast Asian linguistic landscape, using the phonetically complex T…

Cited by 0SourceScholar
2025

Universal Low-Resource Speech Synthesis Via Phoneme Fusion Coordinating Low-Rank Decomposition

ICASSP 2025accepted

Recent advancements in end-to-end text-to-speech models have made significant progress. However, these approaches based on high-resource languages, are inapplicable for low-resource languages, and existing low-resource speech synthesis methods are typically specific to single languages. Consequently…

Cited by 0SourceScholar