Fusion of multiple parameterisations for DNN-based sinusoidal speech synthesis with multi-task learning

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An Overview of Multi-task Learning (2018, neutral): “Applications of MTL in speech include speech synthesis [114,115] and those for natural language processing include joint learning of six NLP tasks (i.”
A Survey on Multi-Task Learning (2017, methodology): “In [196], a multi-task deep neural network is used to model cepstra and log amplitudes as primary and auxiliary tasks for sinusoidal speech synthesis.”
Modeling Prosodic Phrasing With Multi-Task Learning in Tacotron-Based TTS (2020, methodology): “It has also been used in speech synthesis [19], such as statistical parametric speech synthesis with GANs [20] and DNN-based speech synthesis with stacked bottleneck features [21].”
Investigating gated recurrent neural networks for speech synthesis (2016, unknown): “In [4, 5, 6, 7, 8], feed-forward neural networks are employed to map a linguistic representation derived from input text directly to acoustic features.”
Merlin: An Open Source Neural Network Speech Synthesis System (2016, methodology): “The system has already been extensively used for the work reported in a number of recent research papers[30, 26, 22, 20, 31, 32, 23, 33, for example].”