In this paper we adopt several anomaly detection methods to detect annotation errors in single-speaker read-speech corpora used for text-to-speech (TTS) synthesis. 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. Word-level feature sets including basic features derived from forced alignment, and various acoustic, spectral, phonetic, and positional features were examined. Dimensionality reduction techniques were also applied to reduce the number of features. The first results with F1 score being almost 89% show that anomaly detection could help in detecting annotation errors in read-speech corpora for TTS synthesis.
Bibliographic reference. Matoušek, Jindřich / Tihelka, Daniel (2015): "Anomaly-based annotation errors detection in TTS corpora", In INTERSPEECH-2015, 314-318.