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Jinyu Li

Jinyu Li
PRINCIPAL APPLIED SCIENTIST
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I received the Ph.D. degree from Georgia Institute of Technology, Atlanta, in 2008. From 2000 to 2003, I was a Researcher in the Intel China Research Center and Research Manager in iFlytek, China. I joined Microsoft in 2008 and now lead a team to design and improve speech modeling algorithms and technologies that ensure industry state-of-the-art speech recognition accuracy for Microsoft products. My major research interests cover several topics in speech recognition, including deep learning, noise robustness, discriminative training, feature extraction, and machine learning methods.

 

What's New

Selected Honors

  • Microsoft research technical transfer award, 2014
  • Microsoft patent awards, 2008-present
  • Interspeech 2006 Best Student Paper
  • Colonel Oscar P. Cleaver Award (for the highest score on the Ph.D. preliminary exam in ECE, Georgia Institute of Technology, 2004)
  • Guo Moruo Scholarship (the highest honor in USTC)

Thesis

Soft margin estimation for automatic speech recognition,” Georgia Institute of Technology, 2008.

 

Publications

Patent

· Learning Student DNN via Output Distribution
· Variable-Component Deep Neural Network For Robust Speech Recognition
· Shared Hidden Layer Combination for Speech Recognition Systems
· Low-footprint Adaptation and Personalization for Deep Neural Network
· Restructuring deep neural network acoustic models
· Exploiting heterogeneous data in deep neural network based speech recognition systems
· Efficient implementation of posterior-based feature with partial distance elimination
· Multilingual Deep Neural Network
· Utilizing Scalar Operations For Recognizing Utterances During Automatic Speech Recognition In Noisy Environments
· Online distorted speech estimation within an unscented transformation framework
· Confidence Calibration in Automatic Speech Recognition Systems
· Model training for automatic speech recognition from imperfect transcription data
· Phase sensitive model adaptation for noisy speech recognition
· Adapting a compressed model for use in speech recognition
· high-performance hmm adaptation with joint compensation of additive and convolutive distortions via vector Taylor series