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.
Oktem, A., Farrús, M., Bonafonte, A. Visualizing punctuation restoration in speech transcripts with prosograph. A: Annual Conference of the International Speech Communication Association. "Interspeech 2018: 2-6 September 2018, Hyderabad". Baixas: International Speech Communication Association (ISCA), 2018, p. 1493-1494.