Transformer-TTS vs. NaturalSpeech3 vs. Discrete Event TTS: three competing neural architectures for high-fidelity speech synthesis. The diagram contrasts continuous mel-spectrogram prediction (Transformer-TTS with PostNet vocoder), discrete codec token diffusion (NaturalSpeech3 with VQ-VAE), and proposed event-level autoregressive generation (Discrete Event TTS with neural synthesizer). Each pipeline flows from phoneme encoding through acoustic modeling to waveform reconstruction, with distinct loss functions and token representations. Understanding these trade-offs—attention mechanisms, codec quantization, and event tokenization—is critical for selecting the right TTS architecture for latency, quality, and expressiveness requirements. Fork this diagram on Diagrams.so to customize component choices, add OCI compute resources, or benchmark inference costs across architectures.