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The paper introduces SteerDuplex, a full-duplex speech dialogue model with steerable attributes like tone and persona, and presents SteerBench for evaluating spoken steerability, demonstrating improvements over baselines using reinforcement learning.
DialectS2S is an end-to-end speech dialogue model for low-resource Chinese dialects, introducing a scalable data synthesis pipeline and a two-stage post-training strategy with self-aligned speech supervision. Experiments show improvements in dialect consistency, response quality, and intelligibility, with fully open-sourced models, data, and code.
BayLing-Duplex is a native full-duplex speech language model that enables a single autoregressive LLM to manage turn-taking and interruptions without external VAD modules, achieving high success rates and improved response quality over prior models.