@inproceedings{liu-etal-2026-ped,
title = "{PED}: Route-Decoupled Diagnostics for Persona Consistency in Spoken Agents",
author = "Liu, Weihao and
Wei, Junrui and
Zhang, Zhao and
Zhang, Ju",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.445/",
pages = "9155--9168",
ISBN = "979-8-89176-395-1",
abstract = "Maintaining a stable persona is central to sustained spoken role-playing, yet when an agent breaks character, current evaluations often do not isolate which component caused the failure, making fixes slow and ad hoc.We propose PED (Persona-Emotion Decoupling), a diagnostic evaluation framework that decomposes persona expression into two observable routes: what the agent says (text) and how it sounds (speech).PED operationalizes the affective slice of persona expression by projecting transcripts and audio into a shared affective measurement space for route-comparable, reference-based analyses of separability, drift, failures, and coupling.We demonstrate PED via two worked instantiations spanning an end-to-end Speech LLM and a cascaded LLM+TTS pipeline under a fixed dialogue protocol.Within this setting, PED surfaces four recurring diagnostic signatures:(i) route-level separability is bounded by reference overlap and can differ sharply across architectures,(ii) text-route drift is stress-linked and tends toward a neutral-heavy region,(iii) text-audio consistency is weakly coupled, yielding route-asymmetric failures,and (iv) audio-route structure can be materially shaped by an explicit intermediate style cue in cascaded pipelines.Overall, PED reframes holistic ``voice+character'' grading as turn-level, fault-localizing signals for faster debugging and iteration."
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<abstract>Maintaining a stable persona is central to sustained spoken role-playing, yet when an agent breaks character, current evaluations often do not isolate which component caused the failure, making fixes slow and ad hoc.We propose PED (Persona-Emotion Decoupling), a diagnostic evaluation framework that decomposes persona expression into two observable routes: what the agent says (text) and how it sounds (speech).PED operationalizes the affective slice of persona expression by projecting transcripts and audio into a shared affective measurement space for route-comparable, reference-based analyses of separability, drift, failures, and coupling.We demonstrate PED via two worked instantiations spanning an end-to-end Speech LLM and a cascaded LLM+TTS pipeline under a fixed dialogue protocol.Within this setting, PED surfaces four recurring diagnostic signatures:(i) route-level separability is bounded by reference overlap and can differ sharply across architectures,(ii) text-route drift is stress-linked and tends toward a neutral-heavy region,(iii) text-audio consistency is weakly coupled, yielding route-asymmetric failures,and (iv) audio-route structure can be materially shaped by an explicit intermediate style cue in cascaded pipelines.Overall, PED reframes holistic “voice+character” grading as turn-level, fault-localizing signals for faster debugging and iteration.</abstract>
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%0 Conference Proceedings
%T PED: Route-Decoupled Diagnostics for Persona Consistency in Spoken Agents
%A Liu, Weihao
%A Wei, Junrui
%A Zhang, Zhao
%A Zhang, Ju
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-ped
%X Maintaining a stable persona is central to sustained spoken role-playing, yet when an agent breaks character, current evaluations often do not isolate which component caused the failure, making fixes slow and ad hoc.We propose PED (Persona-Emotion Decoupling), a diagnostic evaluation framework that decomposes persona expression into two observable routes: what the agent says (text) and how it sounds (speech).PED operationalizes the affective slice of persona expression by projecting transcripts and audio into a shared affective measurement space for route-comparable, reference-based analyses of separability, drift, failures, and coupling.We demonstrate PED via two worked instantiations spanning an end-to-end Speech LLM and a cascaded LLM+TTS pipeline under a fixed dialogue protocol.Within this setting, PED surfaces four recurring diagnostic signatures:(i) route-level separability is bounded by reference overlap and can differ sharply across architectures,(ii) text-route drift is stress-linked and tends toward a neutral-heavy region,(iii) text-audio consistency is weakly coupled, yielding route-asymmetric failures,and (iv) audio-route structure can be materially shaped by an explicit intermediate style cue in cascaded pipelines.Overall, PED reframes holistic “voice+character” grading as turn-level, fault-localizing signals for faster debugging and iteration.
%U https://aclanthology.org/2026.findings-acl.445/
%P 9155-9168
Markdown (Informal)
[PED: Route-Decoupled Diagnostics for Persona Consistency in Spoken Agents](https://aclanthology.org/2026.findings-acl.445/) (Liu et al., Findings 2026)
ACL