# Crowdsourced and Automatic Speech Prominence Estimation

arXiv: https://arxiv.org/abs/2310.08464 · HTML: https://mlsanity.com/paper/2310.08464/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [WHISTRESS: Enriching Transcriptions with Sentence Stress Detection](https://mlsanity.com/paper/2505.19103/) (2025)
- [StressTest: Can YOUR Speech LM Handle the Stress?](https://mlsanity.com/paper/2505.22765/) (2025)
- [Emphasis Rendering for Conversational Text-to-Speech with Multi-modal Multi-scale Context Modeling](https://mlsanity.com/paper/2410.09524/) (2024)
- [Can LLMs Understand the Implication of Emphasized Sentences in Dialogue?](https://mlsanity.com/paper/2406.11065/) (2024)
- [Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark](https://mlsanity.com/paper/2604.10580/) (2026)
- [Do Speech Emphasis Models Generalize across Languages and Emotions?](https://mlsanity.com/paper/2606.27717/) (2026)

## Abstract (arXiv)

The prominence of a spoken word is the degree to which an average native listener perceives the word as salient or emphasized relative to its context. Speech prominence estimation is the process of assigning a numeric value to the prominence of each word in an utterance. These prominence labels are useful for linguistic analysis, as well as training automated systems to perform emphasis-controlled text-to-speech or emotion recognition. Manually annotating prominence is time-consuming and expensive, which motivates the development of automated methods for speech prominence estimation. However, developing such an automated system using machine-learning methods requires human-annotated training data. Using our system for acquiring such human annotations, we collect and open-source crowdsourced annotations of a portion of the LibriTTS dataset. We use these annotations as ground truth to train a neural speech prominence estimator that generalizes to unseen speakers, datasets, and speaking styles. We investigate design decisions for neural prominence estimation as well as how neural prominence estimation improves as a function of two key factors of annotation cost: dataset size and the number of annotations per utterance.