Voting Detector: A Combination of Anomaly Detectors to Reveal Annotation Errors in TTS Corpora

Anomaly detection techniques were shown to help in detecting word-level annotation errors in read-speech corpora for text-to-speech synthesis. In this framework, correctly annotated words are considered as normal examples on which the detection methods are trained. Misannotated words are then taken as anomalous examples which do not conform to normal patterns of the trained detection models. In this paper we propose a concept of a voting detector — a combination of anomaly detectors in which each “single” detector “votes” on whether a testing word is annotated correctly or not. The final decision is then made by aggregating the votes. Our experiments show that voting detector has a potential to overcome each of the single anomaly detectors.