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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Acoustic Modelsatistical parametric speech synthesis, and then the sequence-to-sequence models based on an encoder-attention-decoder framework (including RNN, CNN, and Transformer), and the latest feed-forward models (CNN or Transformer) and advanced generative models (GAN, Flow, VAE, and Diffusion).

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Basics of Spoken Language Processing-speech synthesis. Since speech and language are studied in the discipline of linguistics, we first overview some basic knowledge in linguistics and discuss a key concept called speech chain that is closely related to TTS. Then, we introduce speech signal processing, which covers the topics of digit

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Text Analysesase speech synthesis. Text analyses consist of several components: (1) text processing, which processes raw text from documents, normalizes the text from the written form into spoken form, and conducts some linguistic analyses; (2) phonetic analysis, which converts text into phonetic symbols, includ

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Acoustic Models the development of TTS, different kinds of acoustic models have been adopted, including the early hidden Markov models and deep neural networks in statistical parametric speech synthesis, and then the sequence-to-sequence models based on an encoder-attention-decoder framework (including RNN, CNN, a

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VocodersTTS, different kinds of vocoders have been adopted, including the vocoders in statistical parametric speech synthesis (SPSS), and neural network-based vocoders. We first view vocoders from a historic perspective, covering vocoders in SPSS and neural TTS, and then introduce the vocoders in neural TTS