What process is central to emotion-aware speech generation (TTS) involving expressive characteristics?
Style transfer, decoupling linguistic content from expressive style
The core technical challenge and success metric for emotion-aware Text-to-Speech (TTS) revolves around style transfer. This complex process necessitates the deep learning model's ability to fundamentally separate the utterance into two distinct components: the linguistic content (the actual words being conveyed, or what is said) and the expressive style (the emotional prosody, tone, and manner of delivery, or how it is said). Once decoupled, the model can take the linguistic information from a neutral input sentence and reconstruct the output using the acoustic style vector—the target emotion—taken from a reference voice. This reconstruction is far more sophisticated than simply concatenating or splicing audio clips; it involves generating novel speech that maintains the semantic meaning of the input text while adopting the precise emotional contour of the reference style, leading to far more natural and engaging machine output.
