Continuous Stochastic Feature Mapping Based on Trajectory HMMs

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Deep Learning: Methods and Applications (2014, background): “Similar trajectory HMMs also form the basis for parametric speech synthesis [228, 326, 439, 438].”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “Similar trajectory HMMs also form the basis for parametric speech synthesis [62–66].”
An Overview of Deep-Structured Learning for Information Processing (2011, methodology): “See also Zen et al., 2011 for the use of trajectory HMMs for feature mapping.).”
A probabilistic interpretation for artificial neural network-based voice conversion (2015, methodology): “Moreover, it is noted that the probabilistic model for the proposed ANN-based VC may encounter an inconsistence between training and conversion when using a dynamic-feature constraint; such inconsistence can degrade the quality of converted speech [3, 4, 22].”
Voice Conversion Using Deep Neural Networks With Layer-Wise Generative Training (2014, background): “This is because the information (e.g. formants) for building the conversion function is usually dif.cult to be extracted accurately.”