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Cambridge university transcription systems for the multi-genre broadcast challenge., , , , , , , and . ASRU, page 639-646. IEEE, (2015)Data Selection for Improving Naturalness of TTS Voices Trained on Small Found Corpuses., , , , , and . SLT, page 319-324. IEEE, (2018)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)Enhanced Virtual Singers Generation by Incorporating Singing Dynamics to Personalized Text-to-speech-to-singing., , , , , and . ICASSP, page 6960-6964. IEEE, (2019)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)Improving lightly supervised training for broadcast transcription., , , , , and . INTERSPEECH, page 2187-2191. ISCA, (2013)Selection and Training Schemes for Improving TTS Voice Built on Found Data., , , and . INTERSPEECH, page 1516-1520. ISCA, (2019)Unsupervised segmentation of triplet Markov chains hidden with long-memory noise, , and . Signal Processing, 88 (5): 1134--1151 (May 2008)Speaker diarisation and longitudinal linking in multi-genre broadcast data., , , , , , , and . ASRU, page 660-666. IEEE, (2015)