JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems

JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/jal-turn-joint-acoustic-linguistic-modeling-for-real-time-and-robust-turn-taking-detection-in-full-duplex-spoken-dialogu

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 6
Coverage: 33%

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Canonical ID jal-turn-joint-acoustic-linguistic-modeling-for-real-time-and-robust-turn-taking-detection-in-full-duplex-spoken-dialogu | Route /signal-canvas/jal-turn-joint-acoustic-linguistic-modeling-for-real-time-and-robust-turn-taking-detection-in-full-duplex-spoken-dialogu

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems

PDF: https://arxiv.org/pdf/2603.26515v1

Source count: 6

Coverage: 33%

Last proof check: 2026-03-30T22:20:01.607Z

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Watch and verify: JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems

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Subject: JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems

Verdict

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
In this paper, we propose JAL-Turn, a lightweight and efficient speech-only turn-taking framework that adopts a joint acoustic–linguistic modeling paradigm
Implicationpartial
This is a core claim stated directly in the abstract and elaborated in the introduction.
Verificationpartial
partial
Evidencepartial
By sharing a frozen ASR encoder, JAL-Turn enables turn-taking prediction to run fully in parallel with speech recognition, introducing no additional end-to-end latency or computational overhead.
Implicationpartial
This technical advantage is explicitly stated in the abstract and further detailed in the method section.
Verificationpartial
partial
Evidencepartial
Extensive experiments on public multilingual benchmarks and an in-house Japanese customer-service dataset show that JAL-Turn consistently outperforms strong state-of-the-art baselines in detection accuracy while maintaining superior real-time performance.
Implicationpartial
This is a primary result claim, directly stated in the abstract and supported by multiple tables in the results section.
Verificationpartial
partial
Evidencepartial
JAL-Turn92.03 0.92538
Implicationpartial
Specific numerical results are provided for accuracy and F1-score on a particular dataset, with a direct comparison to a baseline.
Verificationpartial
partial
Evidencepartial
JAL-Turn operates comfortably within the real-time regime, with end-to-end latencies of 22 ms on the public dataset and 43 ms on the in-house corpus.
Implicationpartial
Specific latency figures are provided for different datasets, demonstrating real-time performance.
Verificationpartial
partial
Evidencepartial
JAL-Turn underperforms on the bc state (80% vs. 91%), which we conjecture stems from the intrinsically context-dependent nature of backchannels
Implicationpartial
This is a specific limitation identified and explained in the results section.
Verificationpartial
partial
Evidencepartial
We introduce a scalable data construction pipeline that automatically derives reliable turn-taking labels from large-scale real-world dialogue corpora without manual annotation
Implicationpartial
This is a key methodological contribution explicitly stated in the abstract and bullet points.
Verificationpartial
partial

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JAL-Turn: Joint Acoustic-Linguistic Modeling for Real-Time and Robust Turn-Taking Detection in Full-Duplex Spoken Dialogue Systems

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