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

arXiv: https://arxiv.org/abs/2104.02410 · HTML: https://mlsanity.com/paper/2104.02410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 143 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [The Impact of Automated Parameter Optimization on Defect Prediction Models](https://mlsanity.com/paper/1801.10270/) (2018)
- [Naming the Pain in Requirements Engineering: Contemporary Problems, Causes, and Effects in Practice](https://mlsanity.com/paper/1611.10288/) (2016)
- [Release early, release often, and watch your users' emotions](https://mlsanity.com/paper/1906.06403/) (2019)
- [The State-of-Practice in Requirements Elicitation: An Extended Interview Study at 12 Companies](https://mlsanity.com/paper/2102.11556/) (2021)
- [Asymmetric Release Planning-Compromising Satisfaction against Dissatisfaction](https://mlsanity.com/paper/1901.05130/) (2019)
- [Do feelings matter? On the correlation of affects and the self-assessed productivity in software engineering](https://mlsanity.com/paper/1408.1293/) (2014)
- [Recognizing Developers' Emotions while Programming](https://mlsanity.com/paper/2001.09177/) (2020)
- [Emotion Detection Using Noninvasive Low Cost Sensors](https://mlsanity.com/paper/1708.06664/) (2017)
- [A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors](https://mlsanity.com/paper/1903.03426/) (2019)
- [Two Elements of Pair Programming Skill](https://mlsanity.com/paper/2102.06460/) (2021)

## Abstract (arXiv)

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.