Text-to-speech (TTS) systems based on neural networks have undergone a significant evolution, taking a step forward towards achieving human-like quality and expressiveness, which is crucial for applications such as social media content creation and voice interfaces for visually impaired individuals. An entire branch of research, known as Expressive Text-to-speech (ETTS), has emerged to address the so-called one-to-many mapping problem, which limits the naturalness of generated output. However, most ETTS systems applying explicit style modeling treat the prediction of prosodic features as a regressive, rather than generative, process and, consequently, do not capture prosodic diversity. We address this problem by proposing a novel technique for inference-time prediction of speaking-style features, which leverages a diffusion framework for sampling from a learned space of Global Style Tokens-based embeddings, which are then used to condition a neural TTS model. By incorporating the diffusion model, we can leverage its powerful modeling capabilities to learn the distribution of possible stylistic features and, during inference, sample them non-deterministically, which makes the generated speech more human-like by alleviating prosodic monotony across multiple sentences. Our system blends a regressive predictor with a diffusion-based generator to enable smooth control over the diversity of generated speech. Through quantitative and qualitative (human-centered) experiments, we demonstrated that our system generates expressive human speech with non-deterministic high-level prosodic features.
Prosowicz et al. (2025) studied this question.