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Titlebook: Neural Text-to-Speech Synthesis; Xu Tan Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nat

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Expressive and Controllable TTSle, emotion, etc. In this chapter, we first conduct a comprehensive analysis of this variation information, and then introduce some technologies for expressive and controllable TTS, including modeling, disentangling, controlling, and transferring this variation information.
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Model-Efficient TTSall memory storage, and low computation cost. However, early neural TTS models usually leverage deep neural networks that are usually with large computation/memory/time costs. In this chapter, we introduce the technologies for model-efficient TTS. We categorize these technologies according to the me
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Data-Efficient TTSd distributions in speech. However, in many scenarios, the training data is usually limited, which hinders the effective learning of neural TTS models. In this chapter, we introduce data-efficient methods to solve the low data resource issue in TTS. Generally speaking, there are different scenarios
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Beyond Text-to-Speech Synthesisynthesis is singing voice synthesis (SVS). Actually, SVS can be regarded as a subtask of TTS, as SVS shares similar input/output, model pipeline, and methodology with TTS. Voice conversion (VC) is also closely related to TTS since they both aim to synthesize speech. The difference is that TTS synthe
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Xu Tanxercises some SAS® programs are provided to replicate the results. Most graphs are plotted using EViews. Some of the problems and solutions are obtained from Econometric Theory (ET) and these are reprinted with the pennission of Cambridge University Press. I would like to thank Peter C. B. Phillips.
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Xu Tanthe textbook results.Econometric software programs to reprodThis manual provides solutions to selected exercises from each chapter of Econometrics by Badi H. Baltagi starting with Chapter 2. For the empirical exercises some SAS® programs are provided to replicate the results. Most graphs are plotted
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