Text and Speech Analysis detailed syllabus for Computer Science & Engineering (CSE) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the CSE students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Computer Science & Engineering 5th Sem scheme and its subjects, do visit CSE 5th Sem 2021 regulation scheme. For Professional Elective-I scheme and its subjects refer to CSE Professional Elective-I syllabus scheme. The detailed syllabus of text and speech analysis is as follows.
Course Objectives:
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Unit I
NATURAL LANGUAGE BASICS 6 Foundations of natural language processing – Language Syntax and Structure- Text Preprocessing and Wrangling – Text tokenization – Stemming – Lemmatization – Removing stop-words – Feature Engineering for Text representation – Bag of Words model- Bag of N-Grams model – TF-IDF model
Suggested Activities
- Flipped classroom on NLP
- Implementation of Text Preprocessing using NLTK
- Implementation of TF-IDF models
Suggested Evaluation Methods
- Quiz on NLP Basics
- Demonstration of Programs
Unit II
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Unit III
QUESTION ANSWERING AND DIALOGUE SYSTEMS 9 Information retrieval – IR-based question answering – knowledge-based question answering -language models for QA – classic QA models – chatbots – Design of dialogue systems -evaluating dialogue systems
Suggested Activities:
- Flipped classroom on language models for QA
- Developing a knowledge-based question-answering system
- Classic QA model development
Suggested Evaluation Methods
- Assignment on the above topics
- Quiz on knowledge-based question answering system
- Development of simple chatbots
Unit IV
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Unit V
AUTOMATIC SPEECH RECOGNITION 6 Speech recognition: Acoustic modelling – Feature Extraction – HMM, HMM-DNN systems
Suggested Activities:
- Flipped classroom on Speech recognition.
- Exploring Feature extraction
Suggested Evaluation Methods
- Assignment on the above topics
- Quiz on acoustic modelling
Practical Exercises
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Course Outcomes:
On completion of the course, the students will be able to
- Explain existing and emerging deep learning architectures for text and speech processing
- Apply deep learning techniques for NLP tasks, language modelling and machine translation
- Explain coreference and coherence for text processing
- Build question-answering systems, chatbots and dialogue systems
- Apply deep learning models for building speech recognition and text-to-speech systems
Text Books:
- Daniel Jurafsky and James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Third Edition, 2022.
Reference Books:
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For detailed syllabus of all the other subjects of Computer Science & Engineering 5th Sem, visit CSE 5th Sem subject syllabuses for 2021 regulation.
For all Computer Science & Engineering results, visit Anna University CSE all semester results direct link.