Language Agnostic Syllabification with Active Learning

This repository contains an implementation of a language-agnostic syllabification method using active learning. Syllabification is the process of splitting a word into syllables, crucial in speech synthesis and recognition. Our approach utilizes active learning to reduce the need for large labeled datasets. By adapting the neural network from Krantz et al. (2019) and training it with active learning, we improved accuracy on the Portuguese and Italian datasets, using only a small fraction of the data: 384 words (1.4% of the dataset) for Portuguese and 528 words (0.6% of the dataset) for Italian.

🚀 Usage

Prerequisites

Before running the project, ensure that you have the following:

• MATLAB 2021a (or a newer version)
• Statistics and Machine Learning Toolbox
• Text Analytics Toolbox

Running the Project

• Clone this repository to your local machine or download the ZIP archive.
• Open MATLAB and navigate to the root directory of the cloned repository.
• Locate the src folder and open the main.m file.
• Run the main.m script to execute the project.

📊 Results

The project showcases its effectiveness by achieving remarkable accuracy values with minimal labeled data. Specifically, the following results were obtained:

• Porlex v3 (Portuguese dataset): Achieved an accuracy of 96.8% using only 384 words, which corresponds to 1.4% of the original dataset.
• PhonItalia (Italian dataset): Achieved an accuracy of 82.0% using only 528 words, which corresponds to 0.6% of the original dataset.
• Lexique 2 (French dataset): Achieved an accuracy of 95.8% using only 208 words, which is less than 0.01% of the whole dataset.

For both Portuguese and Italian, these results surpass those achieved by training the network on the entire dataset, 95.6% and 81%, respectively.

📜 License

This project is licensed under the MIT License.

🎉 Acknowledgments

We would like to acknowledge the work of Krantz et al. (2019) for providing the neural network architecture used in this project. Their research serves as a foundation for our active learning adaptation.

📬 Contact

If you have any questions, suggestions, or just want to say hello, feel free to email me at jmcabralpinto@gmail.com.