The landscape of Text-to-Speech (TTS) technology has undergone a significant transformation in recent years, moving away from traditional rule-based and concatenative methods toward highly expressive, end-to-end neural architectures. Among the most influential contributions to this evolution is Baidu’s Deep Voice series, which has redefined the performance boundaries of real-time speech synthesis. This paper presents a comprehensive review of the Deep Voice models—Deep Voice 1, 2, and 3—highlighting their structural innovations, training paradigms, and improvements in voice fidelity, latency, and speaker adaptability. Beyond Deep Voice, we investigate how competing neural TTS architectures such as Tacotron, WaveNet, and FastSpeech offer alternative pathways to high-quality synthesis. Through comparative analysis, we examine differences in attention mechanisms, autoregressive vs. non-autoregressive modeling, vocoder strategies, and scalability for deployment. Particular emphasis is placed on real-time capabilities, where Deep Voice’s efficient processing pipeline allows for low-latency synthesis suitable for interactive applications like voice assistants, automated narration, and live language translation. In addition to architectural insights, this review explores broader issues shaping the future of TTS systems. These include challenges in prosody modeling, cross-lingual synthesis, and speaker identity preservation. The paper also addresses ethical implications, such as risks of voice cloning, bias in training data, and misuse of synthetic speech. By evaluating both the technological advancements and the societal impacts, we aim to provide a holistic view of the current state and future directions of real-time neural TTS, with Deep Voice serving as a focal point for innovation and ongoing research.

Keywords: Neural Text-to-Speech, Deep Voice, Real-Time Synthesis, Speech Generation, Voice Cloning, Prosody Modeling