  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.
