The purpose of the Automatic Speech Recognition course is to introduce you to the rapidly growing field automatic speech recognition. The course is sectioned into three parts. The first part teaches the background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation while the second part deals with the algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. See More: https://bit.ly/2HksJws

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Automatic Speech Recognition
( 8 REVIEWS ) 698 STUDENTS
The purpose of the course is to introduce you to the rapidly growing eld automatic speech
recognition. The course is …
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9 HOURS, 16 MINUTES
HOME CURRICULUM REVIEWS
The purpose of the Automatic Speech Recognition course is to introduce you to the rapidly growing
eld automatic speech recognition. The course is sectioned into three parts. The rst part teaches
the background material in the acoustic theory of speech production, acoustic-phonetics, and signal
representation while the second part deals with the algorithmic aspects of speech recognition
systems including pattern classi cation, search algorithms, stochastic modelling, and language
modelling techniques. Finally, the last section describes advanced techniques used for acoustic-
phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic
information, speech understanding, and multimodal processing. You will also learn to compare and
contrast the various approaches to speech recognition.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to
complete the course, the answers will be in the form of written work in pdf or word. Students can
write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers
are submitted, the tutor will check and assess the work.
Certification
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Course Credit: MIT
Module 01
Course Overview 00:27:00
Acoustic Theory of Speech Production 00:20:00
Speech Sounds 00:22:00
Signal Representation 00:28:00
Vector Quantization 00:30:00
Pattern Classi cation (1) 00:30:00
Pattern Classi cation (2) 00:26:00
Search 00:25:00
Hidden Markov Modeling (1) 00:28:00
Language Modeling 00:28:00
Guest Lecture by Karen Livescu Graphical Models Quiz 1 00:26:00
Module 02
Guest Lecture by Rita Singh Hidden Markov Modeling (2) 00:26:00
Guest Lecture by Rita Singh Hidden Markov Modeling (3) 00:29:00
Segment-Based ASR 00:24:00
COURSE CURRICULUM

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Guest Lecture by Lee Hetherington Finite State Transducers 00:28:00
Acoustic-Phonetic Modeling 00:25:00
Robust ASR (1) 00:27:00
Guest Lecture by Timothy Hazen Robust ASR (2) 00:29:00
Guest Lecture by Timothy Hazen Adaptation 00:27:00
Speech Understanding 00:23:00
Guest Lecture by Timothy Hazen Paralinguistic Information 00:28:00
Assessment
Submit Your Assignment 00:00:00
Certi cation 00:00:00
COURSE REVIEWS
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