The purpose of the [course_title] 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. 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.
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.
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Course Credit: MIT
|Acoustic Theory of Speech Production||00:20:00|
|Pattern Classification (1)||00:30:00|
|Pattern Classification (2)||00:26:00|
|Hidden Markov Modeling (1)||00:28:00|
|Guest Lecture by Karen Livescu Graphical Models Quiz 1||00:26:00|
|Guest Lecture by Rita Singh Hidden Markov Modeling (2)||00:26:00|
|Guest Lecture by Rita Singh Hidden Markov Modeling (3)||00:29:00|
|Guest Lecture by Lee Hetherington Finite State Transducers||00:28:00|
|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|
|Guest Lecture by Timothy Hazen Paralinguistic Information||00:28:00|
|Submit Your Assignment||00:00:00|
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