语音识别系统

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.

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微软
edX
  • 完成时间大约为 4
  • 高级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Fundamentals of Speech Recognition

Basic Signal Processing for Speech Recogntion

Acoustic Modeling and Labeling

Common Algorithms for Language Modeling

Decoding Acoustic Features into Speech

课程概况

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 causetheir 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.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

预备知识

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

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