How to apply prosody and speech acts to TTS

How You Can Apply My Work to Your Projects

If your team is working on voice AI, TTS (Text-to-Speech), or speech-based interaction systems, the insights from my prosodic-functional analysis can help you create more intentional, human-like output. But how exactly can this linguistic research be applied?

Let’s break it down with a concrete example:

Imagine you're building or refining a TTS system for Italian. I analyze utterances based on their communicative function (e.g., greeting, thanking, requesting, asserting) and their prosodic structure—pitch contour, intensity, and duration. For example:

  • Function: Greeting.

  • Prosodic pattern: Final melodic rise (+7 semitones), increased intensity (+17 dB), and elongated duration (~4 seconds).

  • Common form in writing: Ciaoooo (with expressive vowel extension to mirror speech melody).

What Your TTS Team Can Do With This Analysis

1. Prepare and Enrich Your Datasets (Data Curation)
Use my function-based classification to annotate your corpus with communicative labels (e.g., [Greeting], [Thanking], [Request]). This enables your models to be trained not only on phonetic or emotional cues, but on communicative intent.

2. Model Functional Prosody (Prosody Modeling)
You can use my detailed pitch, duration, and intensity measurements to condition your model (e.g., Tacotron, FastSpeech, VITS) to produce more natural-sounding outputs tailored by function. Functional embeddings or conditioning vectors can reflect these distinctions.

3. Create Input Markup That Signals Intent (Text as Prosodic Proxy)
You can adopt my writing recommendations—such as using “Ciaoooo” to suggest elongation—as part of your model’s input preprocessing. TTS engines that support markup (like Amazon Polly with SSML) can be fine-tuned with this style of annotation, or custom parsers can interpret labels like [Greeting-long].

4. Refine Audio Output (Post-Processing)
If your synthesis pipeline allows post-processing, you can use the prosodic curves I provide as reference to manually fine-tune pitch, duration, and intensity using audio manipulation tools.

5. Evaluate Output With Human Expectations in Mind (Intent Matching)
My linguistic model helps your team check if the generated output aligns with human expectations for that function. You can develop evaluation criteria based on whether the synthetic voice delivers the intended speech act—e.g., Does this really sound like a greeting?

All of this can be integrated during:

  • Model training (with annotated corpora),

  • Preprocessing (by adapting input text),

  • or Postprocessing (adjusting synthesized audio directly).