User satisfaction plays a crucial role in user experience (UX) evaluation. Traditionally, UX measurements are based on subjective scales, such as questionnaires. However, these evaluations may suffer from subjective bias. In this paper, we explore the acoustic and prosodic features of speech to differentiate between positive and neutral UX during interactive sessions. By analyzing speech features such as root-mean-square (RMS), zero-crossing rate(ZCR), jitter, and shimmer, we identified significant differences between the positive and neutral user groups. In addition, social speech features such as activity and engagement also show notable variations between these groups. Our findings underscore the potential of speech analysis as an objective and reliable tool for UX measurement, contributing to more robust and bias-resistant evaluation methodologies. This work offers a novel approach to integrating speech features into UX evaluation and opens avenues for further research in HCI.

翻译：用户满意度在用户体验评估中起着至关重要的作用。传统上，用户体验的测量主要基于主观量表，例如问卷调查。然而，这些评估方法可能受到主观偏差的影响。本文通过分析语音的声学和韵律特征，以区分交互会话中积极与中性的用户体验。通过分析均方根、过零率、基频微扰和振幅微扰等语音特征，我们发现了积极用户组与中性用户组之间存在显著差异。此外，活动度和参与度等社交语音特征在这些组间也表现出明显变化。我们的研究结果强调了语音分析作为一种客观可靠的用户体验测量工具的潜力，有助于建立更稳健且抗偏差的评估方法。这项工作为将语音特征整合到用户体验评估中提供了一种新颖的途径，并为未来的人机交互研究开辟了新的方向。