Comunicació presentada a: Interspeech 2018, celebrat del 2 al 6 de setembre de 2018 a Hyderabad, Índia.

We have developed a neural architecture that tests the effect of lexical, morphosyntactic and prosodic features in restoring punctuation in speech transcriptions. Having outperformed a baseline model in terms of precision and recall, we further extend our performance tests by attaching it in a speech recognition pipeline. The visual and interactive testing environment that we prepared helps us observe how our models generalizes in unseen data and also plan our next steps for improvement.

The first author has received Maria de Maeztu Reproducibility Award from Department of Information and Communication Technologies of Universitat Pompeu Fabra in 2018 through presentation of this work. The second author is funded by the Spanish Ministry of Economy, Industry and Competitiveness through the Ram´on y Cajal program.