Paralinguistic and non-linguistic aspects of speech strongly influence listener impressions. While most research focuses on absolute impression scoring, this study investigates relative voice impression estimation (RIE), a framework for predicting the perceptual difference between two utterances from the same speaker. The estimation target is a low-dimensional vector derived from subjective evaluations, quantifying the perceptual shift of the second utterance relative to the first along an antonymic axis (e.g., ``Dark--Bright''). To isolate expressive and prosodic variation, we used recordings of a professional speaker reading a text in various styles. We compare three modeling approaches: classical acoustic features commonly used for speech emotion recognition, self-supervised speech representations, and multimodal large language models (MLLMs). Our results demonstrate that models using self-supervised representations outperform methods with classical acoustic features, particularly in capturing complex and dynamic impressions (e.g., ``Cold--Warm'') where classical features fail. In contrast, current MLLMs prove unreliable for this fine-grained pairwise task. This study provides the first systematic investigation of RIE and demonstrates the strength of self-supervised speech models in capturing subtle perceptual variations.

翻译：语音的副语言和非语言特征对听者印象产生显著影响。现有研究多集中于绝对印象评分，而本研究探讨相对语音印象估计框架，旨在预测同一说话者两段话语之间的感知差异。估计目标为基于主观评价导出的低维向量，该向量沿反义轴量化第二段话语相对于第一段话语的感知偏移。为分离表达性与韵律变化，我们采用专业朗读者以不同风格朗读同一文本的录音数据。我们比较了三种建模方法：常用于语音情感识别的经典声学特征、自监督语音表征以及多模态大语言模型。实验结果表明，采用自监督表征的模型在捕捉复杂动态印象方面优于经典声学特征方法，特别是在经典特征失效的维度上。相比之下，当前多模态大语言模型在此细粒度成对任务中表现不可靠。本研究首次系统探讨了相对语音印象估计问题，并证明了自监督语音模型在捕捉细微感知变化方面的优势。