# Pitch Accent Detection improves Pretrained Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2508.04814 · HTML: https://mlsanity.com/paper/2508.04814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [Improved Noisy Student Training for Automatic Speech Recognition](https://mlsanity.com/paper/2005.09629/) (2020)
- [The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling](https://mlsanity.com/paper/2011.11588/) (2020)
- [Iterative Pseudo-Labeling for Speech Recognition](https://mlsanity.com/paper/2005.09267/) (2020)
- [DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization](https://mlsanity.com/paper/2012.06659/) (2020)
- [Effectiveness of self-supervised pre-training for speech recognition](https://mlsanity.com/paper/1911.03912/) (2019)
- [SlimIPL: Language-Model-Free Iterative Pseudo-Labeling](https://mlsanity.com/paper/2010.11524/) (2020)
- [The Zero Resource Speech Challenge 2017](https://mlsanity.com/paper/1712.04313/) (2017)
- [The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units](https://mlsanity.com/paper/2010.05967/) (2020)
- [ProsAudit, a prosodic benchmark for self-supervised speech models](https://mlsanity.com/paper/2302.12057/) (2023)
- [Detection of Prosodic Boundaries in Speech Using Wav2Vec 2.0](https://mlsanity.com/paper/2209.15032/) (2022)
- [A neural prosody encoder for end-ro-end dialogue act classification](https://mlsanity.com/paper/2205.05590/) (2022)
- [Improving End-to-End SLU performance with Prosodic Attention and Distillation](https://mlsanity.com/paper/2305.08067/) (2023)
- [Data augmentation using prosody and false starts to recognize non-native children's speech](https://mlsanity.com/paper/2008.12914/) (2020)
- [CNN Encoding of Acoustic Parameters for Prominence Detection](https://mlsanity.com/paper/2104.05488/) (2021)

## Built on by (most cited first, 1 of 1)

- [Does a prosody-trained representation help beyond trainable fusion? A parameter-matched study with frozen HuBERT](https://mlsanity.com/paper/2609.36754/) (2026)

## Abstract (arXiv)

We show the performance of Automatic Speech Recognition (ASR) systems that use semi-supervised speech representations can be boosted by a complimentary pitch accent detection module, by introducing a joint ASR and pitch accent detection model. The pitch accent detection component of our model achieves a significant improvement on the state-of-the-art for the task, closing the gap in F1-score by 41%. Additionally, the ASR performance in joint training decreases WER by 28.3% on LibriSpeech, under limited resource fine-tuning. With these results, we show the importance of extending pretrained speech models to retain or re-learn important prosodic cues such as pitch accent.