Speech Recognition Systems
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About this course:
Developing and understanding Automatic Speech Recognition (ASR) systems is an inter-disciplinary activity, taking expertise in linguistics, computer science, mathematics, and electrical engineering.
When a human speaks a word, they cause their voice to make a time-varying pattern of sounds. These sounds are waves of pressure that propagate through the air. The sounds are captured by a sensor, such as a microphone or microphone array, and turned into a sequence of numbers representing the pressure change over time. The automatic speech recognition system converts this time-pressure signal into a time-frequency-energy signal. It has been trained on a curated set of labeled speech sounds, and labels the sounds it is presented with. These acoustic labels are combined with a model of word pronunciation and a model of word sequences, to create a textual representation of what was said.
Instead of exploring one part of this process deeply, this course is designed to give an overview of the components of a modern ASR system. In each lecture, we describe a component's purpose and general structure. In each lab, the student creates a functioning block of the system. At the end of the course, we will have built a speech recognition system almost entirely out of Python code.
- Fundamentals of Speech Recognition
- Basic Signal Processing for Speech Recogntion
- Acoustic Modeling and Labeling
- Common Algorithms for Language Modeling
- Decoding Acoustic Features into Speech
- Speech processing anaylst
- Voice recognition system specialist
- Linguistic theorist
- Some python experience
- Basic Machine Learning principles
- Knowledge of probability and statistics
|Learning Style||Self-Paced Learning|
|Course Duration||24 Hours|