Entrainment-Metrics: An Open-Source Toolkit for Quantifying Acoustic-Prosodic Entrainment in Spoken Dialogue

Abstract

Speech entrainment, particularly acoustic-prosodic (a/p) entrainment, has gained significant interest in recent years for its implications on social interaction and dialogue systems. However, measuring a/p entrainment is challenging, mainly due to the scarce consensus on its characterization, and the lack of software libraries readily available to non-expert programmers. This paper presents Entrainment-Metrics, an open-source Python toolkit for quantifying a/p entrainment in spoken dialogue. It allows users to quantify different characterizations of a/p entrainment, measure its occurrence along different a/p features, and choose between different measurement strategies. The toolkit was designed for researchers and practitioners from a wide range of fields, with no need for a strong computational background, who are interested in studying a/p entrainment and its relation to other phenomena. This paper provides a concise introduction to using Entrainment-Metrics and presents an illustrative empirical study that highlights its capabilities.