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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 0Source ↗Scholar ↗
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 0Source ↗Scholar ↗