Applying speech and NLP techniques to improve the automatic video/ audio transcription and summarization system.
Machine Learning Intern
Apple Inc.
Implemented state-of-the-art model efficiency techniques for Transformer models.
Achieved a 2.1x speedup and reduced parameters by 25% while maintaining translation quality by applying knowledge distillation, simpler recurrent architecture, and pruning techniques on Transformer.
Visiting Researcher
AI Research Group, Vector Institute; University of Toronto
Developed early pathological voice detection models using speech processing and deep learning techniques.
Built a system to solve the channel mismatch problem between devices, increasing the target domain PR-AUC from 0.84 to 0.94, using an unsupervised domain adaptation method, domain adversarial training.
Detected dementia in a low-resource language by proposing a transfer learning method.
Research Assistant
Bio-Acoustic Signal Processing Lab, Academia Sinica
Proposed novel neural network structures that achieves a 4x compression rate and 1.2x acceleration without performance degradation by quantizing the floating-point weights.
Integrated and optimized deep learning-based models (LSTM, FCN…) for various signal processing tasks, including speech enhancement and disease detection.