Annotation error detection: Anomaly detection vs. classification

Abstract

We compare two approaches to automatic detection of annotation errors in single-speaker read-speech corpora used for speech synthesis: anomaly- and classification-based detection. Both approaches principally differ in that the classification-based approach needs to use both correctly annotated and misannotated words for training. On the other hand, the anomaly-based detection approach needs only the correctly annotated words for training (plus a few misannotated words for validation). We show that both approaches lead to statistically comparable results when all available misannotated words are utilized during detector/classifier development. However, when a smaller number of misannotated words are used, the anomaly detection framework clearly outperforms the classification-based approach. A final listening test showed the effectiveness of the annotation error detection for improving the quality of synthetic speech.