Decode Before You Hear: Speculative Listening for Chunk-Interleaved Full-Duplex Speech Language Models
The paper studies chunk-interleaved full-duplex speech language models, which process audio in fixed time chunks with uneven workloads: listen chunks leave compute idle, while speak and tool-call chunks require serial generation.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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The paper studies chunk-interleaved full-duplex speech language models, which process audio in fixed time chunks with uneven workloads: listen chunks leave compute idle, while speak and tool-call chunks require serial generation. The authors observe that with silence as the assumed input, outputs match the real-audio outputs in 95.3% of chunks, and 88% of differences begin at the first output token. They propose Speculative Listening, a training-free, lossless framework that drafts the next chunk during idle time under silence and a runner-up first-token branch, then verifies drafts when audio arrives. On TickTock-Bench, the authors report 2.6-2.8x speedups on speak chunks and up to 23.4x on tool-call chunks.
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Results are on the authors' own eight-task TickTock-Bench across four models; source claims not independently checked, and the full article was reviewed only via the supplied abstract-level text.
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Results are on the authors' own eight-task TickTock-Bench across four models; source claims not independently checked, and the full article was reviewed only via the supplied abstract-level text.
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With silence as the assumed input, 95.3% of chunk outputs match outputs produced from real audio, per the paper.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphrase88% of output divergences appear at the very first token, motivating a runner-up first-token branch.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseOn TickTock-Bench, speak chunks speed up 2.6-2.8x and tool-call chunks up to 23.4x.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseDecoding delay before a response starts drops 73% on average, to 74-241 ms.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
Results are on the authors' own eight-task TickTock-Bench across four models; source claims not independently checked, and the full article was reviewed only via the supplied abstract-level text.
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Why this may matter
Full-duplex voice agents need responses and tool calls to keep pace with live audio. A lossless, training-free way to use idle compute could reduce conversational latency without changing model outputs.
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Results are on the authors' own eight-task TickTock-Bench across four models; source claims not independently checked, and the full article was reviewed only via the supplied abstract-level text.
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