Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in
  Spoken Language Models
Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought
(CoT) reasoning due to the prohibitive latency of generating the entire thought
process sequentially. Enabling SLMs to think while speaking, similar to humans,
is attracting increasing attention. We present, for the first time, Mind-Paced
Speaking (MPS), a brain-inspired framework that enables high-fidelity,
real-time reasoning. Similar to how humans utilize distinct brain regions for
thinking and responding, we propose a novel dual-brain approach, employing a
"Formulation Brain" for high-level reasoning to pace and guide a separate
"Articulation Brain" for fluent speech generation. This division of labor
eliminates mode-switching, preserving the integrity of the reasoning process.
Experiments show that MPS significantly outperforms existing
think-while-speaking methods and achieves reasoning performance comparable to
models that pre-compute the full CoT before speaking, while drastically
reducing latency. Under a zero-latency configuration, the proposed method
achieves an accuracy of 92.8% on the mathematical reasoning task Spoken-MQA and
attains a score of 82.5 on the speech conversation task URO-Bench. Our work
effectively bridges the gap between high-quality reasoning and real-time
interaction.
68ec5bc2cd07fb414898c8f7
Donghang Wu
68ec5bc2cd07fb414898c8f8
Haoyang Zhang
68ec5bc2cd07fb414898c8f9
Jun Chen
68ec5bc2cd07fb414898c8fa
Xiangyu
68ec5bc2cd07fb414898c8fb
Zhang
68ec5bc2cd07fb414898c8fc
Hexin Liu
68ec5bc2cd07fb414898c8fd
Eng Siong Chng
68ec5bc2cd07fb414898c8fe
Fei Tian
68ec5bc2cd07fb414898c8ff
Xuerui Yang
68ec5bc2cd07fb414898c900
Xiangyu Zhang
68ec5bc2cd07fb414898c901
Daxin Jiang
68ec5bc2cd07fb414898c902
Gang Yu
Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in
  Spoken Language Models
Real-time Spoken Language Models (SLMs) struggle to leverage Chain-of-Thought
(CoT) reasoning due to the prohibitive latency of generating the entire thought
process sequentially. Enabling SLMs to think while speaking, similar to humans,
is attracting increasing attention. We present, for the first time, Mind-Paced
Speaking (MPS), a brain-inspired framework that enables high-fidelity,
real-time reasoning. Similar to how humans utilize distinct brain regions for
thinking and responding, we propose a novel dual-brain approach, employing a
"Formulation Brain" for high-level reasoning to pace and guide a separate
"Articulation Brain" for fluent speech generation. This division of labor
eliminates mode-switching, preserving the integrity of the reasoning process.
Experiments show that MPS significantly outperforms existing
think-while-speaking methods and achieves reasoning performance comparable to
models that pre-compute the full CoT before speaking, while drastically
reducing latency. Under a zero-latency configuration, the proposed method
achieves an accuracy of 92.8% on the mathematical reasoning task Spoken-MQA and
attains a score of 82.5 on the speech conversation task URO-Bench. Our work
effectively bridges the gap between high-quality reasoning and real-time
interaction.
Mind-Paced Speaking (MPS) is a brain-inspired framework that enables real-time reasoning and fluent speech generation by dividing the process into a "Formulation Brain" for reasoning and an "Articulation Brain" for speech, achieving high accuracy with low latency.
Chain-of-Thought (CoT) reasoning Mind-Paced Speaking (MPS) dual-brain approach Formulation Brain Articulation Brain real-time reasoning Spoken-MQA URO-Bench