# Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0

arXiv: https://arxiv.org/abs/2209.15032 · HTML: https://mlsanity.com/paper/2209.15032/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2022.

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

- [Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models](https://mlsanity.com/paper/2306.04980/) (2023)
- [Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model](https://mlsanity.com/paper/2306.02579/) (2023)
- [Unsupervised speech intelligibility assessment with utterance level alignment distance between teacher and learner Wav2Vec-2.0 representations](https://mlsanity.com/paper/2306.08845/) (2023)
- [Pitch Accent Detection improves Pretrained Automatic Speech Recognition](https://mlsanity.com/paper/2508.04814/) (2025)
- [Assessing Phrase Break of ESL speech with Pre-trained Language Models](https://mlsanity.com/paper/2210.16029/) (2022)
- [Prominence-aware automatic speech recognition for conversational speech](https://mlsanity.com/paper/2509.10116/) (2025)

## Abstract (arXiv)

Prosodic boundaries in speech are of great relevance to both speech synthesis and audio annotation. In this paper, we apply the wav2vec 2.0 framework to the task of detecting these boundaries in speech signal, using only acoustic information. We test the approach on a set of recordings of Czech broadcast news, labeled by phonetic experts, and compare it to an existing text-based predictor, which uses the transcripts of the same data. Despite using a relatively small amount of labeled data, the wav2vec2 model achieves an accuracy of 94% and F1 measure of 83% on within-sentence prosodic boundaries (or 95% and 89% on all prosodic boundaries), outperforming the text-based approach. However, by combining the outputs of the two different models we can improve the results even further.