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Random Forests with Random Projections of the Output Space for High Dimensional Multi-label Classification.

, , and . ECML/PKDD (1), volume 8724 of Lecture Notes in Computer Science, page 607-622. Springer, (2014)

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Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech., , , , , , and . CoRR, (2020)Multi-Scale Spectrogram Modelling for Neural Text-to-Speech., , , , , , , , and . CoRR, (2021)CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech., , , , , and . INTERSPEECH, page 4387-4391. ISCA, (2020)L1-based compression of random forest models., , , and . ESANN, (2012)Globally Induced Forest: A Prepruning Compression Scheme., , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 420-428. PMLR, (2017)Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody., , , , , , , and . CoRR, (2022)BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data., , , , , , , , , and 9 other author(s). CoRR, (2024)Controllable Emphasis with zero data for text-to-speech., , , , , , , , , and 4 other author(s). CoRR, (2023)Distribution Augmentation for Low-Resource Expressive Text-To-Speech., , , , , , , , , and 1 other author(s). ICASSP, page 8307-8311. IEEE, (2022)Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech., , , , , , and . ICASSP, page 6573-6577. IEEE, (2021)