Advanced Text-to-Speech Synthesis
This chapter shifts focus to the generation of speech, detailing the methods used to build modern Text-to-Speech (TTS) systems. The aim is to progress from basic synthesis concepts to techniques capable of producing high-fidelity, natural-sounding, and controllable artificial voices.
You will examine the architecture and training processes for several categories of state-of-the-art acoustic models:
- Autoregressive Models: Analyzing sequence-to-sequence approaches like Tacotron and Transformer-based TTS.
- Non-Autoregressive Models: Studying parallel generation techniques for faster inference, such as FastSpeech and its variants.
- Flow-Based and GAN-Based Models: Investigating alternative generative modeling paradigms applied to acoustic feature generation.
Beyond the core model architectures, we will cover methods for:
- Modeling and controlling speech prosody (rhythm, intonation).
- Generating expressive speech with varying styles or emotions.
- Implementing voice cloning and conversion systems.
The chapter includes a hands-on practical section focused on training an advanced TTS model using a contemporary toolkit.
Sections
4.1 Autoregressive Acoustic Models (Tacotron, Transformer TTS)
4.2 Non-Autoregressive Acoustic Models (FastSpeech, ParaNet)
4.3 Flow-Based Models for TTS
4.4 Generative Adversarial Networks (GANs) in TTS
4.5 Prosody Modeling and Control
4.6 Expressive Speech Synthesis
4.7 Voice Cloning and Conversion
4.8 Hands-on Practical: Training an Advanced TTS Model
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