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Course Outline
Introduction to Speech Recognition and Synthesis
- Fundamentals of speech technologies
- Basics of speech recognition systems
- Overview of speech synthesis
Role of LLMs in Speech Technologies
- Understanding LLMs in speech recognition
- LLMs in speech synthesis
- Advantages of LLMs over traditional models
Data for Speech Recognition and Synthesis
- Data collection and processing for speech technologies
- Training data sets for LLMs
- Ethical considerations in data handling
Training LLMs for Speech Applications
- Deep learning techniques in speech recognition
- Neural network architectures for speech synthesis
- Fine-tuning LLMs for specific speech tasks
Implementing LLMs in Speech Systems
- Integration of LLMs with speech recognition engines
- Developing natural-sounding speech synthesizers
- User interface design for speech applications
Testing and Evaluating Speech Systems
- Methods for testing speech recognition accuracy
- Evaluating the naturalness of synthesized speech
- User studies and feedback collection
Challenges and Solutions in Speech Technologies
- Addressing common issues in speech recognition
- Overcoming obstacles in speech synthesis
- Case studies: successful implementations of LLMs
Future Directions in Speech Technologies
- Emerging trends in speech recognition and synthesis
- The role of LLMs in multilingual speech systems
- Innovations and research opportunities
Project and Assessment
- Designing and implementing a speech recognition or synthesis system using LLMs
- Peer reviews and group discussions
- Final assessment and feedback
Summary and Next Steps
Requirements
- An understanding of basic programming concepts
- Experience with Python programming is recommended but not required
- Familiarity with basic machine learning and neural network concepts is beneficial
Audience
- Software developers
- Data scientists
- Product managers
14 Hours
