Example Concept: Neural network-based models, such as Tacotron 2 and WaveNet, are commonly used to generate realistic vocal transitions. These models learn from large datasets of human speech to understand natural prosody, intonation, and rhythm, enabling them to produce seamless transitions in synthesized speech. By adjusting parameters like pitch, speed, and emphasis, these tools can further refine the naturalness of the vocal output.