*Data and Reproducibility*

From the whole corpus, we have used data corresponding to 'Free dialogues' and 'Task dialogues' in order to make the results more comparable with a previous work [4] which was based in the analysis of spontaneous speech conversation. Glissando corpus is a freely accessible corpus for non-commercial uses. It can be obtained through ELRA (http://catalog.elra.info/en-us/repository/browse/ELRA-S0406/ for the Spanish subcorpus and http://catalog.elra.info/en-us/repository/browse/ELRA-S0407/ for the Catalan subcorpus.).

Post-processed data of the Glissando corpus created by the authors of this article are available at https://doi.org/10.6071/M3XW9T, while scripts for generating the results are available at https: //github.com/ivangtorre/ling-law-speech-spanish-catalan. We used Python 3.7 for the analysis. Levenberg–Marquardt algorithm, Kolmogorov–Smirnov distance, Spearman test and most of MLE fits use Scipy 1.3.0. MLE fits for power laws that are self-coded. Other libraries such as Numpy 1.16.2, Pandas 0.24.2 or Matplotlib 3.1.0 were also used. Fits to Zipf's law are done with R and PowerRlaw [46].

**Author Contributions:** All authors contributed to conceptualization, methodology, investigation, validation and writing (original draft preparation and review and editing of final version). J.-M.G. provided the database and data curation. I.G.T. performed data postprocessing, data analysis and programming; L.L. and A.H.-F. performed the formal analysis, supervision, project administration, resources and funding acquisition.

**Funding:** I.G.T. was supported by the gran<sup>t</sup> FIS2017-84151-P (Ministerio de Economia, Industria y Competitividad, Gobierno de España). A.H.-F. was supported by the gran<sup>t</sup> TIN2017-89244-R (MACDA) (Ministerio de Economia, Industria y Competitividad, Gobierno de España). I.G.T. and A.H.-F. were supported by the project PRO2019-S03 (RCO03080 Lingüística Quantitativa) de l'Institut d'Estudis Catalans. The development of the Glissando corpus was supported by projects FFI2008-04982-C03-01 and FFI2011-29559-C02-01 of the Ministerio de Ciencia e Innovación (Gobierno de España). L.L. was supported by EPSRC Early Career Fellowship EP/P01660X/1.

**Acknowledgments:** The authors especially appreciate the previous work of all collaborators in the Glissando corpus, without which this work would not have been possible.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
