Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information about the engagement of the user on the discussed product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biometric data (F1=0.72), and that voice features alone are sufficiently effective (F1=0.71). Our work contributes with one the first studies in requirements engineering in which biometrics are used to identify emotions. This is also the first study in software engineering that considers voice analysis. The usage of voice features could be particularly helpful for emotion-aware requirements elicitation in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research.

翻译：捕捉用户参与度对于收集软件产品功能反馈至关重要。在市场驱动背景下，当前收集和分析用户反馈的方法主要基于从产品评论和社交媒体中提取信息的技术。这些方法难以适用于定制软件开发，或需要从特定用户收集信息的场景。在此类情况下，企业需通过面对面访谈获取产品反馈。本文提出利用生理与语音特征构成的生物特征数据，为访谈补充用户对产品相关议题的参与度信息。我们通过访谈用户同时采集其生理数据（即使用Empatica E4腕带获取生物反馈）及普通笔记本电脑内置录音设备捕获语音的方式评估该方法。实验结果表明：基于生物特征数据训练监督机器学习算法可预测用户参与度（F1=0.72），且仅使用语音特征已具备足够有效性（F1=0.71）。本研究作为需求工程领域首批利用生物特征识别情感的研究之一，亦是软件工程中首次引入语音分析的工作。语音特征的应用对远程沟通中的情感感知需求获取具有特殊价值——无论是通过人工分析师还是语音聊天机器人实现，同时也可用于支持软件工程研究中的会议分析。