Pause Prediction from Text for Speech Synthesis with User-Definable Pause Insertion Likelihood Threshold

Predicting the location of pauses from text is an important aspect for speech synthesizers. The accuracy of pause prediction can significantly influence both naturalness and intelligibility. Pauses which help listeners to better parse the synthesized speech into meaningful units are deemed to increase naturalness and intelligibility ratings, while pauses in unexpected or incorrect locations can reduce these ratings and cause confusion. This paper presents a multi-stage pause prediction approach including first prosodic chunk prediction, followed by a feature scoring algorithm and finally a pause sequence evaluation module. Preference tests showed that the new method outperformed a pauses-at-punctuation baseline while not yet matching human performance. In addition, the approach includes two more functionalities: (1) a user-specifiable pause insertion rate and (2) multiple output formats in the form of binary pauses, multi-level pauses or as a score reflecting pause strength.