A Comprehensive Review of Expressive Text-To-Speech Systems and its Advancements and Challenges
P Matan, P Velvizhy · 2025
Text-to-speech (TTS) technology has advanced significantly, evolving from early rule-based systems to modern deep learning models that produce natural and intelligible speech. However, while current TTS systems excel in speech clarity, they often lack emotional depth and expressiveness, which are crucial for human-like communication. This paper reviews the development of expressive TTS, focusing on techniques that enhance the emotional and prosodic aspects of synthesized speech. We examine the key components of traditional TTS systems and explore how recent innovations such as contextual and style-based synthesis, emotion-aware models, and data augmentation techniques have addressed these limitations. Special attention is given to the integration of sentiment analysis and prosodic control, which enable more dynamic and engaging speech. Despite these advancements, challenges remain in achieving multilingual expressiveness, fine-tuning speech features, and ensuring real-time performance. The paper concludes by discussing future directions in expressive TTS, including the integration of multimodal cues, use of large diverse datasets, and the potential application of reinforcement learning to improve emotional modulation. These advancements hold the promise of creating more immersive and emotionally intelligent systems for applications in virtual assistants, audiobooks, and assistive technologies.