CRF-Based Phrase Boundary Detection Trained on Large-Scale TTS Speech Corpora

摘要

The paper compares different approaches in the phrase boundary detection issue, based on the data gained from speech corpora recorded for the purpose of the text-to-speech (TTS) system. It is showed that conditional random fields model can outperform basic deterministic and classification-based algorithms both in speaker-dependent and speaker independent phrasing. The results on manually annotated sentences with phrase breaks are presented here as well.