PTCCS369 Syllabus - Text And Speech Analysis - 2023 Regulation Anna University

PTCCS369 Syllabus - Text And Speech Analysis - 2023 Regulation Anna University

PTCCS369

TEXT AND SPEECH ANALYSIS

 L T P C

2 0 2 3

COURSE OBJECTIVES:
• Understand natural language processing basics
• Apply classification algorithms to text documents
• Build question-answering and dialogue systems
• Develop a speech recognition system
• Develop a speech synthesizer

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

6

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

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

TEXT-TO-SPEECH SYNTHESIS

6

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

30 PERIODS

PRACTICAL EXERCISES: 30 PERIODS
1. Create Regular expressions in Python for detecting word patterns and tokenizing text
2. Getting started with Python and NLTK - Searching Text, Counting Vocabulary, Frequency Distribution, Collocations, Bigrams
3. Accessing Text Corpora using NLTK in Python
4. Write a function that finds the 50 most frequently occurring words of a text that are not stop words.
5. Implement the Word2Vec model
6. Use a transformer for implementing classification
7. Design a chatbot with a simple dialog system
8. Convert text to speech and find accuracy
9. Design a speech recognition system and find the error rate

TOTAL: 60 PERIODS

COURSE OUTCOMES: On completion of the course, the students will be able to
CO1: Explain existing and emerging deep learning architectures for text and speech processing
CO2: Apply deep learning techniques for NLP tasks, language modelling and machine translation
CO3: Explain coreference and coherence for text processing
CO4: Build question-answering systems, chatbots and dialogue systems
CO5: Apply deep learning models for building speech recognition and text-to-speech systems

TEXT BOOKS:
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:
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.

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