CCS369 Text and Speech Analysis [PDF]

Anna University – CCS369 Text and Speech Analysis Regulation 2021  Notes Book, Syllabus , Important Questions, Question Paper with Answers Previous Year Question Paper.

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 TEXT CLASSIFICATION CCS369 Text and Speech Analysis
Vector Semantics and Embeddings -Word Embeddings – Word2Vec model – Glove model –
FastText model – Overview of Deep Learning models – RNN – Transformers – Overview of Text
summarization and Topic Models
Suggested Activities
 Flipped classroom on Feature extraction of documents
 Implementation of SVM models for text classification
 External learning: Text summarization and Topic models
Suggested Evaluation Methods
 Assignment on above topics
 Quiz on RNN, Transformers
 Implementing NLP with RNN and Transformers

UNIT III QUESTION ANSWERING AND DIALOGUE SYSTEMS CCS369 Text and Speech Analysis
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 TEXT-TO-SPEECH SYNTHESIS CCS369 Text and Speech Analysis
Overview. Text normalization. Letter-to-sound. Prosody, Evaluation. Signal processing –
Concatenative and parametric approaches, WaveNet and other deep learning-based TTS
systems
Suggested Activities:
 Flipped classroom on Speech signal processing
 Exploring Text normalization
 Data collection
 Implementation of TTS systems
Suggested Evaluation Methods
 Assignment on the above topics
 Quiz on wavenet, deep learning-based TTS systems
 Finding accuracy with different TTS systems

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

TEXTBOOK CCS369 Text and Speech Analysis
1. Daniel Jurafsky and James H. Martin, “Speech and Language Processing: An Introduction
to Natural Language Processing, Computational Linguistics, and Speech Recognition”,
Third Edition, 2022.

REFERENCES: CCS369 Text and Speech Analysis
1. Dipanjan Sarkar, “Text Analytics with Python: A Practical Real-World approach to Gaining
Actionable insights from your data”, APress,2018.
2. Tanveer Siddiqui, Tiwary U S, “Natural Language Processing and Information Retrieval”,
Oxford University Press, 2008.
3. Lawrence Rabiner, Biing-Hwang Juang, B. Yegnanarayana, “Fundamentals of Speech
Recognition” 1st Edition, Pearson, 2009.
4. Steven Bird, Ewan Klein, and Edward Loper, “Natural language processing with Python”,
O’REILLY.