5 papers
When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning
Ruixiang Mao, Xiangnan Ma, Dan Chen +12
Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive p…
SageLM: A Multi-aspect and Explainable Large Language Model for Speech Judgement
Yuan Ge, Junxiang Zhang, Xiaoqian Liu +10
Speech-to-Speech (S2S) Large Language Models (LLMs) are foundational to natural human-computer interaction, enabling end-to-end spoken dialogue systems. However, evaluating these m…
M-CIF: Multi-Scale Alignment For CIF-Based Non-Autoregressive ASR
Ruixiang Mao, Xiangnan Ma, Qing Yang +7
The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanism creates a smooth and monotonic mappin…
EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs
Yuhao Zhang, Yuhao Du, Zhanchen Dai +4
Speech-to-speech large language models (SLLMs) are attracting increasing attention. Derived from text-based large language models (LLMs), SLLMs often exhibit degradation in knowled…
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation
Yuhao Zhang, Xiangnan Ma, Kaiqi Kou +7
The success of building textless speech-to-speech translation (S2ST) models has attracted much attention. However, S2ST still faces two main challenges: 1) extracting linguistic fe…