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Description
  • We investigate the problem of automatic detection of annotation errors in single-speaker read-speech corpora used for text-to-speech (TTS) synthesis. Various word-level feature sets were used, and the performance of several detection methods based on support vector machines, extremely randomized trees, k-nearest neighbors, and the performance of novelty and outlier detection are evaluated. We show that both word- and utterance-level annotation error detections perform very well with both high precision and recall scores and with F1 measure being almost 90%, or 97%, respectively.
  • We investigate the problem of automatic detection of annotation errors in single-speaker read-speech corpora used for text-to-speech (TTS) synthesis. Various word-level feature sets were used, and the performance of several detection methods based on support vector machines, extremely randomized trees, k-nearest neighbors, and the performance of novelty and outlier detection are evaluated. We show that both word- and utterance-level annotation error detections perform very well with both high precision and recall scores and with F1 measure being almost 90%, or 97%, respectively. (en)
Title
  • Annotation Errors Detection in TTS Corpora
  • Annotation Errors Detection in TTS Corpora (en)
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  • Annotation Errors Detection in TTS Corpora
  • Annotation Errors Detection in TTS Corpora (en)
skos:notation
  • RIV/49777513:23520/13:43919427!RIV14-TA0-23520___
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  • P(TA01030476)
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  • 61298
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  • RIV/49777513:23520/13:43919427
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  • speech synthesis; read speech corpora; novelty detection; classification; annotation error detection (en)
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  • [996DCB4BABDF]
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  • Lyon
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  • Red Hook, NY
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  • Proceedings of the 14th Annual Conference of the International Speech Communication Association (Interspeech 2013)
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  • Matoušek, Jindřich
  • Tihelka, Daniel
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number of pages
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  • Curran Associates, Inc.
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  • 978-1-62993-443-3
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  • 23520
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