# Deep Learning For Prominence Detection In Children's Read Speech

arXiv: https://arxiv.org/abs/2110.14273 · HTML: https://mlsanity.com/paper/2110.14273/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations](https://mlsanity.com/paper/1908.02262/) (2019)
- [Prosodic Prominence and Boundaries in Sequence-to-Sequence Speech Synthesis](https://mlsanity.com/paper/2006.15967/) (2020)
- [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)
- [Effects of Word Embeddings on Neural Network-based Pitch Accent Detection](https://mlsanity.com/paper/1805.05237/) (2018)
- [Multitask Learning for Polyphonic Piano Transcription, a Case Study](https://mlsanity.com/paper/1902.04390/) (2019)

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

- [EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models](https://mlsanity.com/paper/2312.14069/) (2023)
- [What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model](https://mlsanity.com/paper/2306.06524/) (2023)
- [Can LLMs Understand the Implication of Emphasized Sentences in Dialogue?](https://mlsanity.com/paper/2406.11065/) (2024)
- [Deep Learning for Assessment of Oral Reading Fluency](https://mlsanity.com/paper/2405.19426/) (2024)
- [Emphasis Sensitivity in Speech Representations](https://mlsanity.com/paper/2508.11566/) (2025)
- [Do Speech Emphasis Models Generalize across Languages and Emotions?](https://mlsanity.com/paper/2606.27717/) (2026)

## Abstract (arXiv)

The detection of perceived prominence in speech has attracted approaches ranging from the design of linguistic knowledge-based acoustic features to the automatic feature learning from suprasegmental attributes such as pitch and intensity contours. We present here, in contrast, a system that operates directly on segmented speech waveforms to learn features relevant to prominent word detection for children's oral fluency assessment. The chosen CRNN (convolutional recurrent neural network) framework, incorporating both word-level features and sequence information, is found to benefit from the perceptually motivated SincNet filters as the first convolutional layer. We further explore the benefits of the linguistic association between the prosodic events of phrase boundary and prominence with different multi-task architectures. Matching the previously reported performance on the same dataset of a random forest ensemble predictor trained on carefully chosen hand-crafted acoustic features, we evaluate further the possibly complementary information from hand-crafted acoustic and pre-trained lexical features.