Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models

ACL ARR 2026 January Submission2437 Authors

Keywords: Spoken Language Models, Multi-turn, Speaking Style

Abstract:

In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a $\textit{multi-turn}$ conversation, they cannot maintain the required speaking styles after several turns of interaction; we refer to this as the $\textbf{style amnesia}$ of SLMs.
We focus on paralinguistic speaking styles, including emotion, accent, volume, and speaking speed.
We evaluate three proprietary and two open-source SLMs, demonstrating that none of these models can maintain a consistent speaking style when instructed to do so.
We further show that when SLMs are asked to recall the style instruction in later turns, they can recall the style instruction, but they fail to express it throughout the conversation.
We also show that explicitly asking the model to recall the style instruction can partially mitigate style amnesia.
In addition, we examine various prompting strategies and find that SLMs struggle to follow the required style when the instruction is placed in system messages rather than user messages, which contradicts the intended function of system prompts.

Paper Type: Long

Research Area: Speech Processing and Spoken Language Understanding

Research Area Keywords: spoken dialog

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 2437