Unlocking Speech Recognition: Deep Learning in Acoustics
Explore the techniques of AI communication by developing speech-to-text models using TensorFlow and PyTorch. This course will teach you the essential techniques to build advanced speech-to-text models, turning spoken words into actionable commands.
What you'll learn
Speech recognition technology offers seamless communication between users and digital responses. Accurately processing speech requires an understanding of technical complexities and natural variation.
In this course, Unlocking Speech Recognition: Deep Learning in Acoustics, you’ll gain the ability to develop sophisticated speech-to-text models that can accurately interpret human speech and respond appropriately.
First, you’ll explore the basics of sound data and feature extraction, gaining an understanding of how to process and prepare audio signals for analysis. Next, you’ll discover the process of designing and training robust speech recognition models, employing cutting-edge neural networks to capture the nuances of human speech.
Finally, you’ll learn how to enhance your model's accuracy by tackling common challenges such as background noise and varying accents. When you’re finished with this course, you’ll have the skills and knowledge of speech recognition technology needed to implement effective speech-to-text systems, which will lead to more natural human-device interactions.
Table of contents
- Strategies for Hyperparameter Tuning 1m
- Demo Advanced Modeling Techniques 4m
- Demo Enhance Model Robustness against Noisy Data 3m
- Balancing between Model Complexity and Performance 2m
- Speech Recognition in Real World Applications 2m
- Demo Enhancing an ASR Model with Advanced Techniques 6m
- Next Steps 0m
