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TorchDIVA: An extensible computational model of speech production built on an open-source machine learning library.
Sean P Kinahan1,2, Julie M Liss1, Visar Berisha1,2
1College of Health Solutions, Arizona State University, Tempe, Arizona, United States of America.
Plos One
|February 17, 2023
Summary
TorchDIVA, a Python-based speech motor control model, closely replicates the original DIVA model. This new implementation facilitates machine learning integration and speech quality enhancement, demonstrating its value for researchers.
Area of Science:
- Computational Neuroscience
- Speech Technology
- Machine Learning
Background:
- The DIVA model simulates speech motor control, integrating brain regions and vocal tract dynamics.
- Current implementation in Matlab Simulink limits integration with Python's extensive machine learning tools.
Purpose of the Study:
- To rebuild the DIVA model in Python using PyTorch for enhanced machine learning compatibility.
- To demonstrate TorchDIVA's capability for speech quality enhancement through generative vocoder integration.
Main Methods:
- Direct translation of DIVA source code from Matlab to Python.
- Reimplementation of Simulink signal blocks from scratch.
- Validation of TorchDIVA module accuracy against the original DIVA model.
- Integration of DiffWave, a PyTorch generative vocoder, for speech enhancement.
Main Results:
- TorchDIVA outputs closely match the original DIVA model with negligible differences.
- Improved speech quality metrics observed in DiffWave-enhanced TorchDIVA output compared to baseline.
- Successful demonstration of TorchDIVA's extensibility as a research platform.
Conclusions:
- TorchDIVA provides a valuable, Python-based platform for speech motor control research.
- The new implementation overcomes limitations of the previous Matlab version, enabling easier integration of advanced machine learning techniques.
- TorchDIVA facilitates research into speech production and quality enhancement.
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