Speech rhythm and intonation in text-to-speech (TTS) systems are generated through a combination of linguistic analysis, machine learning models, and parametric synthesis. These elements fall under prosody—the patterns of stress, pitch, and timing that make speech sound natural. Here’s a breakdown of how they work:
Rhythm is determined by modeling the duration of phonemes (speech sounds) and pauses between words. Modern TTS systems use neural networks trained on annotated speech data to predict how long each phoneme should last. For example, stressed syllables in words like "record" (noun vs. verb) are lengthened, and punctuation (e.g., commas) triggers pauses. Systems like FastSpeech explicitly use duration predictors to align text with rhythmic patterns. Contextual factors—like sentence structure, word emphasis, or speaking rate—adjust these predictions. For instance, in "I want coffee, not tea," the system might lengthen "coffee" and "tea" to contrast them.
Intonation (pitch variation) is generated using pitch prediction models that create F0 (fundamental frequency) contours. Neural TTS architectures like Tacotron 2 or WaveNet analyze text for syntactic and semantic cues to determine pitch patterns. A question ("Really?") might get a rising pitch, while a statement ("Really.") uses a falling contour. Emotion or emphasis also plays a role: words like "amazing" in an excited sentence would have a wider pitch range. These models learn from diverse speech datasets to mimic natural pitch fluctuations, avoiding the robotic flatness of older rule-based systems.
Integration happens through parametric synthesis, where rhythm and intonation parameters are combined with acoustic features to generate speech waveforms. For example, a TTS pipeline might first predict phoneme durations (rhythm), then compute pitch contours (intonation), and finally synthesize audio using a vocoder. Advanced systems allow fine-grained control via SSML tags (e.g., <prosody rate="slow">) or adapt to user preferences by adjusting prosody models during inference. This layered approach ensures natural-sounding speech that mirrors human variability in pacing and melody.