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data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language.

, , , , , and . CoRR, (2022)

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Hierarchical Generative Modeling for Controllable Speech Synthesis., , , , , , , , , and 2 other author(s). CoRR, (2018)Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data., and . CoRR, (2018)A Study of Enhancement, Augmentation and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition., , , and . INTERSPEECH, page 2928-2932. ISCA, (2018)Neural Attention for Learning to Rank Questions in Community Question Answering., , , , , , , , and . COLING, page 1734-1745. ACL, (2016)Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis., , , , and . CoRR, (2018)Learning Latent Representations for Speech Generation and Transformation., , and . INTERSPEECH, page 1273-1277. ISCA, (2017)Scalable Factorized Hierarchical Variational Autoencoder Training., and . INTERSPEECH, page 1462-1466. ISCA, (2018)Exploiting Depth and Highway Connections in Convolutional Recurrent Deep Neural Networks for Speech Recognition., , , and . INTERSPEECH, page 395-399. ISCA, (2016)Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization., , , , , , and . ICASSP, page 5901-5905. IEEE, (2019)Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling., , , , , , , , , and 48 other author(s). CoRR, (2019)