Guangzhi Sun

Disentangling Linguistic and Paralinguistic Information with Routed Sparse Autoencoders

Self-supervised speech encoders contain linguistic and paralinguistic information in a shared, entangled representation space. We combine a TopK sparse autoencoder with route-specific supervision and cross-factor adversaries. Across frozen SPEAR and WavLM encoders, independent probes show factor-specific retention and...

Talk2Agent: Benchmarking Voice Interfaces for Text Agents

Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular interface for interacting with such systems. Speech input introduces an additional failure point: transcription errors can alter task-critical entities, constraints, or ta...

Towards general auditory intelligence for machine listening and speaking

SALMONN-2: Advancing General-Purpose Hearing Abilities with Self-Supervised Representations

A multi-layer feature fusion (MLF) adapter that aggregates information from all encoder layers before projecting them into the language model is proposed and shows that MICL does not emerge naturally in ALLMs, but can be effectively acquired through targeted contextual biasing training.