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Speech Recognition Systems

Learn about the pieces of a modern automatic speech recognition (ASR) system as we cover fundamental acoustic and linguistic theory, data preparation, language modeling, acoustic modeling, and decoding.

Self-Paced

Learning Style

Advanced

Difficulty

24 Hours

Course Duration

Course Info

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Learn about the pieces of a modern automatic speech recognition (ASR) system as we cover fundamental acoustic and linguistic theory, data preparation, language modeling, acoustic modeling, and decoding.

Course Information

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.

Course Objective:

  • Fundamentals of Speech Recognition
  • Basic Signal Processing for Speech Recogntion
  • Acoustic Modeling and Labeling
  • Common Algorithms for Language Modeling
  • Decoding Acoustic Features into Speech

Audience:

  • Speech processing anaylst
  • Voice recognition system specialist
  • Linguistic theorist

Prerequisite:

  • Some python experience
  • Basic Machine Learning principles
  • Knowledge of probability and statistics

Outline

More Information

More Information
Brand Microsoft
Subjects App Development
Lab Access No
Technology Microsoft
Learning Style Self-Paced Learning
Difficulty Advanced
Course Duration 24 Hours
Language English

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