Text and Speech Analysis detailed syllabus for Information Technology (IT) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the IT 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 Information Technology 6th Sem scheme and its subjects, do visit IT 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to IT Professional Elective-VII 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 Information Technology 6th Sem, visit IT 6th Sem subject syllabuses for 2021 regulation.
For all Information Technology results, visit Anna University IT all semester results direct link.