# CNN Encoding of Acoustic Parameters for Prominence Detection

arXiv: https://arxiv.org/abs/2104.05488 · HTML: https://mlsanity.com/paper/2104.05488/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [The role of context in neural pitch accent detection in English](https://mlsanity.com/paper/2004.14846/) (2020)
- [Prosodic Event Recognition using Convolutional Neural Networks with Context Information](https://mlsanity.com/paper/1706.00741/) (2017)

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

- [Pitch Accent Detection improves Pretrained Automatic Speech Recognition](https://mlsanity.com/paper/2508.04814/) (2025)

## Abstract (arXiv)

Expressive reading, considered the defining attribute of oral reading fluency, comprises the prosodic realization of phrasing and prominence. In the context of evaluating oral reading, it helps to establish the speaker's comprehension of the text. We consider a labeled dataset of children's reading recordings for the speaker-independent detection of prominent words using acoustic-prosodic and lexico-syntactic features. A previous well-tuned random forest ensemble predictor is replaced by an RNN sequence classifier to exploit potential context dependency across the longer utterance. Further, deep learning is applied to obtain word-level features from low-level acoustic contours of fundamental frequency, intensity and spectral shape in an end-to-end fashion. Performance comparisons are presented across the different feature types and across different feature learning architectures for prominent word prediction to draw insights wherever possible.