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.
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Acknowledgments
This research was supported by the Czech Science Foundation (GA CR), project No. GA16-04420S. The access to the MetaCentrum clusters provided under the programme LM2015042 is highly appreciated.
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Matoušek, J., Tihelka, D. (2017). Annotation Error Detection: Anomaly Detection vs. Classification. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_13
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DOI: https://doi.org/10.1007/978-3-319-66429-3_13
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