Capturing users’ engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collecting and analyzing users’ feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in contexts where online feedback is limited, as for the majority of apps, and software in general. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this article, we propose to utilize biometric data, in terms of physiological and voice features, to complement product feedback interviews with information about the engagement of the user on 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 biofeedback and voice data, and that voice features alone can be sufficiently effective. The best configurations evaluated achieve an average F1 ∼ 70% in terms of classification performance, and use voice features only. This work is one of the first studies in requirements engineering in which biometrics are used to identify emotions. Furthermore, this is one of the first studies in software engineering that considers voice analysis. The usage of voice features can be particularly helpful for emotion-aware feedback collection 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.

吸引用户的参与对于收集有关软件产品功能的反馈至关重要。在市场驱动的背景下，当前收集和分析用户反馈的方法基于利用从产品评论和社交媒体中提取的信息的技术。这些方法几乎不适用于在线反馈有限的环境，就像大多数应用程序和一般软件一样。在这种情况下，公司需要采取面对面访谈的方式来获取有关其产品的反馈。在本文中，我们建议利用生理和语音特征方面的生物识别数据，通过有关用户参与产品相关主题的信息来补充产品反馈访谈。我们通过采访用户来评估我们的方法，同时使用 Empatica E4 腕带收集他们的生理数据（即生物反馈），并通过普通笔记本电脑的默认录音机捕获他们的声音。我们的结果表明，我们可以通过在生物反馈和语音数据上训练监督机器学习算法来预测用户的参与度，并且仅语音特征就足够有效。评估的最佳配置在分类性能方面达到平均 F1 ~ 70%，并且仅使用语音特征。这项工作是需求工程中最早使用生物识别技术来识别情绪的研究之一。此外，这是软件工程中最早考虑语音分析的研究之一。语音功能的使用对于远程通信中的情感感知反馈收集特别有帮助，无论是由人类分析师还是基于语音的聊天机器人执行，也可以用来支持软件工程研究中的会议分析。