Versión en Español ## Entrainment metrics **entrainment-metrics** is a Python library for measuring the degree of *entrainment* in a dialogue, defined as a speaker's tendency to adapt their speech to match their interlocutor's. Given a dialogue between two speakers, this library outputs three entrainment measures (*proximity*, *convergence* and *synchrony*) on a number of acoustic-prosodic features such as pitch, intensity and speech rate. * Repository: https://github.com/erikernst4/entrainment-metrics * Documentation: https://entrainment-metrics.readthedocs.io * Getting Started page: https://entrainment-metrics.readthedocs.io/en/latest/usage/getting_started.html#getting-started * Notebook tutorial with step-by-step guide: https://github.com/erikernst4/entrainment-metrics/blob/master/examples/entrainment-example.ipynb * **Reference:** E. Ernst, R.H. Gálvez, A. Gravano. "[Entrainment-metrics: An open-source toolkit for quantifying acoustic-prosodic entrainment in spoken dialogue](files/entrainment_metrics___iberamia_2024.pdf)", in: Correia, Rosá & Garijo (eds.), Advances in Artificial Intelligence - IBERAMIA 2024. LNCS, vol 15277, Springer, Feb 2025. [https://doi.org/10.1007/978-3-031-80366-6_33](doi.org/10.1007/978-3-031-80366-6_33) We hope this will be a useful tool for researchers and practitioners working in the fields of speech analysis and spoken dialogue systems. --- **Last updated:** 25 Feb 2025