Authors
- Wang Weiying
- Nakajima Akinori
Abstract
One precondition of effective oral communication is that words should be pronounced clearly, especially for non-native speakers. Word stress is the key to clear and correct English, and misplacement of syllable stress may lead to misunderstandings. Thus, knowing the stress level is important for English speakers and learners.
This paper presents a self-attention model to identify the stress level for each syllable of spoken English.
Key Results:
- Simplest model achieves over 88% accuracy on one dataset
- Over 93% accuracy on another dataset
- More advanced models provide even higher accuracy
1. Introduction
Effective oral communication requires clear pronunciation, particularly for non-native English speakers. Word stress placement is crucial for intelligibility—misplacing syllable stress can lead to misunderstandings or communication breakdowns.
This research addresses the challenge of automatically detecting stress levels at the syllable level, which has applications in:
- Online meetings - Real-time pronunciation feedback
- English learning - Helping learners improve stress patterns
- Speech analysis - Automated assessment of spoken English
2. Methodology
Features Explored
The model analyzes various prosodic and categorical features:
| Feature Type | Description |
|---|
| Pitch Level | Fundamental frequency of the syllable |
| Intensity | Loudness/amplitude of the syllable |
| Duration | Length of the syllable in time |
| Syllable Type | Classification of syllable structure |
| Nuclei Features | Properties of the vowel (nucleus) in each syllable |
Self-Attention Architecture
The self-attention mechanism allows the model to:
- Consider relationships between syllables in a word
- Weight the importance of different prosodic features
- Capture contextual patterns in stress assignment
Input: Prosodic features for each syllable
↓
Self-Attention Layers
↓
Output: Stress level prediction per syllable
3. Results
| Model Version | Dataset 1 | Dataset 2 |
|---|
| Simplest Model | 88%+ | 93%+ |
| Advanced Models | Higher | Higher |
The self-attention architecture proves effective for stress detection, capturing the contextual relationships between syllables that determine stress patterns.
4. Applications
Online Meetings
Real-time pronunciation feedback during video conferences to help non-native speakers communicate more clearly.
English Learning
- Automated pronunciation assessment
- Stress pattern training and correction
- Personalized feedback for learners
Speech Analysis
- Linguistic research on prosodic patterns
- Quality assessment for speech synthesis
- Accent analysis and training
5. Conclusion
This study demonstrates that self-attention models are promising for syllable-level stress detection in spoken English. The approach:
- Achieves high accuracy (88-93%+) across different datasets
- Effectively combines prosodic and categorical features
- Has practical applications in language learning and communication tools
Resources
Citation
@article{wang2023detecting,
title={Detecting Syllable-Level Pronunciation Stress with A Self-Attention Model},
author={Wang, Weiying and Nakajima, Akinori},
journal={arXiv preprint arXiv:2311.00301},
year={2023}
}
