Humanized Language Generation and Discrimination in Scholarly Communication

Humanized Language Generation and Identification

Professor Tao Jianhua

human-friendly language generation

1. Background introduction

1. Machine Learning Advances Rapidly

   • Promote the development and application of language and image fields

2. limitation

   • A large amount of structured data is required, and the performance of small data is poor

3. voice instance

   • A practical language recognition system requires a large amount of labeled data
   • It takes more than ten hours for a single person to synthesize a language close to a real person, but there is very little personalized language data
     

4. challenge

• In many cases, it is difficult to obtain a large amount of speech annotation data
• Minor languages

5. small data modeling
   • Leverage 'Big Data' -> 'Small Data'
     

2. Transfer Learning

3. Speech generation related work

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Speech Generation Reporting Work

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human language identification

1. Language confrontation - generating speech discrimination

2. Research status at home and abroad

3. Propose work