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The development of the cambridge university alignment systems for the multi-genre broadcast challenge.

, , , , , , , and . ASRU, page 647-653. IEEE, (2015)

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Data Selection for Improving Naturalness of TTS Voices Trained on Small Found Corpuses., , , , , and . SLT, page 319-324. IEEE, (2018)Cambridge university transcription systems for the multi-genre broadcast challenge., , , , , , , and . ASRU, page 639-646. IEEE, (2015)Automatic Transcription of Multi-genre Media Archives., , , , , , , , , and 2 other author(s). SLAM@INTERSPEECH, volume 1012 of CEUR Workshop Proceedings, page 26-31. CEUR-WS.org, (2013)The development of the cambridge university alignment systems for the multi-genre broadcast challenge., , , , , , , and . ASRU, page 647-653. IEEE, (2015)Objective evaluation of the Dynamic Model Selection method for spectral voice conversion., and . ICASSP, page 5132-5135. IEEE, (2011)Enhanced Virtual Singers Generation by Incorporating Singing Dynamics to Personalized Text-to-speech-to-singing., , , , , and . ICASSP, page 6960-6964. IEEE, (2019)Selection and Training Schemes for Improving TTS Voice Built on Found Data., , , and . INTERSPEECH, page 1516-1520. ISCA, (2019)Improving lightly supervised training for broadcast transcription., , , , , and . INTERSPEECH, page 2187-2191. ISCA, (2013)Unsupervised segmentation of triplet Markov chains hidden with long-memory noise, , and . Signal Processing, 88 (5): 1134--1151 (May 2008)Unsupervised restoration of hidden nonstationary Markov chains using evidential priors., and . IEEE Trans. Signal Process., 53 (8-2): 3091-3098 (2005)