An Efficient Speech Synthesizer: A Hybrid Monotonic Architecture for Text-to-speech via VAE & LPC-Net with Independent Sentence Length

The article explores the transformative potential of text-to-speech (TTS) technology, which converts written text into spoken language, making information accessible to a broader audience. It delves into the three key components of a 'talking computer's brain': text analysis, acoustic modeling, and vocoding, each playing a crucial role in generating natural-sounding speech. The text discusses the evolution of TTS synthesis, from early concatenative methods to advanced neural network-based approaches, highlighting the challenges and advancements in achieving naturalness and expressiveness. A significant portion of the article is dedicated to a proposed hybrid architecture that combines autoregressive and nonautoregressive models, aiming to generate high-quality speech with low latency and reduced complexity. This architecture leverages variational autoencoders (VAE) for prosody and phoneme extraction and LPC-Net for efficient vocoding, demonstrating superior performance in real-time speech synthesis. The article also addresses the ethical considerations and future prospects of TTS technology, emphasizing the need for responsible deployment and ongoing innovation in this field.