Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models
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 multi-turn conversation, they cannot maintain the required speaking styles after several turns of interaction; we refer to this as the 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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the identification and analysis of "style amnesia" in spoken language models, providing valuable insights into their limitations in maintaining consistent speaking styles across multi-turn interactions. This work is significant as it addresses a critical gap in the evaluation of SLMs, paving the way for future advancements in the field.
Methodology
The paper presents a novel investigation into the phenomenon of "style amnesia" in spoken language models (SLMs), focusing on their ability to maintain specific speaking styles across multi-turn conversations. The methodology includes a structured evaluation framework that assesses SLMs' adherence to style instructions through a user simulator and automatic judges for various speaking styles. The approach is well-defined, utilizing a combination of emotional, accentual, volume, and speed attributes, which adds depth to the analysis. However, the reliance on automatic judges may introduce some variability in results, and the paper could benefit from a more detailed explanation of the evaluation metrics used.
Results & evaluation
The experiments are comprehensive, involving multiple SLMs, including both proprietary and open-source models. The authors provide detailed results on the first turn instruction-following rates and degradation rates across turns, highlighting the challenges faced by SLMs in maintaining style consistency. The use of a user simulator to create realistic multi-turn dialogues is a strong point, although the paper could improve by including more diverse dialogue topics and styles in future experiments. The findings are presented clearly, with appropriate tables and figures to support the analysis.
Reproducibility
The paper outlines the experimental setup and methodology in sufficient detail to allow for reproducibility. However, the absence of publicly available code or datasets limits the ability of other researchers to replicate the results fully. The authors mention using proprietary models, which may not be accessible to all researchers, further complicating reproducibility efforts.
Limitations
The study acknowledges several limitations, including the focus on single-style instructions rather than composite styles, which could be more representative of real-world applications. Additionally, the paper notes that the automatic judges used for style evaluation may not be fully reliable, and the performance of SLMs may vary significantly based on the specific attributes being evaluated. The authors also highlight the need for further research to address the fundamental limitations of SLMs in maintaining style consistency.
Broader impact
The findings have significant implications for the development of more effective spoken language models, particularly in applications requiring consistent emotional expression, such as virtual assistants and interactive dialogue systems. Understanding and mitigating style amnesia could enhance user experience in conversational AI, making interactions feel more natural and engaging. The research could also inform future work on improving the architecture and training of SLMs to better handle multi-turn conversations. The main contribution of this paper is the identification and analysis of "style amnesia" in spoken language models, providing valuable insights into their limitations in maintaining consistent speaking styles across multi-turn interactions. This work is significant as it addresses a critical gap in the evaluation of SLMs, paving the way for future advancements in the field.
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