Prosody prediction in text-to-speech (TTS) models is improving through advancements in neural architectures, explicit prosodic feature modeling, and better training strategies. One key development is the shift to transformer-based models and non-autoregressive architectures. Transformers, with their self-attention mechanisms, capture long-range dependencies in text and audio, enabling more accurate modeling of pitch, rhythm, and stress patterns. For example, models like FastSpeech 2 use non-autoregressive approaches with parallel token generation, which reduces prosody inconsistencies caused by sequential generation in older autoregressive models. These architectures also incorporate variance adaptors to predict pitch, duration, and energy explicitly, allowing finer control over prosody. This reduces the "flatness" of synthesized speech by separating prosodic features from linguistic content during training.
Another improvement comes from leveraging large, diverse datasets and explicit linguistic annotations. Modern TTS systems train on multi-speaker datasets that include varied emotional tones, speaking styles, and contextual scenarios, enabling models to generalize better across prosodic patterns. Additionally, integrating linguistic features like part-of-speech tags, syntactic boundaries, or semantic emphasis helps models align prosody with sentence structure. For instance, a model might emphasize nouns differently from verbs based on syntactic labels, or adjust pitch contours for questions versus statements. Some systems also use external tools (e.g., text-based emotion classifiers) to condition prosody predictions on higher-level context, ensuring intonation matches the intended emotion or discourse function.
Finally, advancements in alignment techniques and evaluation metrics are refining prosody accuracy. Traditional TTS models often struggled with alignment errors, leading to mispronunciations or unnatural pauses. Modern approaches use monotonic alignment strategies (e.g., the duration predictor in FastSpeech) or external alignment models to tightly synchronize text and speech units. Additionally, prosody-specific evaluation metrics—such as pitch contour similarity or pause distribution analysis—are supplementing generic quality scores like Mean Opinion Score (MOS). Techniques like adversarial training, where a discriminator network evaluates prosody naturalness, further push models to generate contextually appropriate rhythms and intonations. These combined efforts allow TTS systems to produce speech with prosody that adapts dynamically to content and context, moving closer to human-like expressiveness.