Using Voice and Biofeedback to Predict User Engagement during Product Feedback Interviews

TL;DR

This study explores using biometric and voice data to predict user engagement during product feedback interviews, demonstrating effective machine learning models that can enhance requirements gathering and emotion detection in software engineering.

Contribution

It introduces a novel approach combining physiological and voice features to assess user engagement, pioneering the use of biometrics and voice analysis in requirements engineering.

Findings

Biometric data can predict user engagement with F1=0.72.

Voice features alone can predict engagement with F1=0.71.

First study using biometrics and voice analysis for emotion detection in requirements engineering.

Abstract

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.