Automatic detection and correction of syntax-based prosody annotation errors

Abstract

Both unit-selection and HMM-based speech synthesis require large annotated speech corpora. To generate more natural speech, considering the prosodic nature of each phoneme of the corpus is crucial. Generally, phonemes are assigned labels which should reflect their suprasegmental characteristics. Labels often result from an automatic syntactic analysis, without checking the acoustic realization of the phoneme in the corpus. This leads to numerous errors because syntax and prosody do not always coincide. This paper proposes a method to reduce the amount of labeling errors, using acoustic information. It is applicable as a post-process to any syntax-driven prosody labeling. Acoustic features are considered, to check the syntax-based labels and suggest potential modifications. The proposed technique has the advantage of not requiring a manually prosody-labelled corpus. The evaluation on a corpus in French shows that more than 75% of the errors detected by the method are effective errors which must be corrected.