• Credit Structure: 1-0-0-0-1

This course provides a foundational understanding of Speech Processing and Natural Language Processing (NLP). It focuses on the core principles, techniques, and applications. The course covers text processing, language models, speech signal processing, automatic speech recognition (ASR), and text-to-speech synthesis (TTS).

Unit 1: Lexical Processing in NLP (2 Hours)

• • What is NLP & Speech Processing

  • Text Normalization: Tokenization, Stemming, Lemmatization.

  • Text Similarity using  MED

Unit 2: Syntactic Processing in NLP  (2 Hours)

• • Context-Free Grammars and Constituency Parsing.

  • Dependency Parsing.

Unit 3: Semantic Processing in NLP  (2 Hours)

• • Word Senses

  • Thesaurus-based algorithms

  • Distributional algorithms

Unit 4: Phonetics & Speech Signal Processing (3 Hours)

• • Basics of Speech Production & Phonetics.

  • Feature Extraction: MFCC, Spectrograms, PLP Features.

  • Deep Learning for Speech: Introduction to Wave2Vec, WavLM.

Unit 5: Automatic Speech Recognition (ASR) & Text-to-Speech (TTS) (3 Hours)

• • ASR Pipeline: Feature extraction → Acoustic modeling → Decoding.

  • HMM vs. DNN-based ASR systems.

  • End-to-End ASR Models: Wave2Vec, Whisper API.

  • TTS Pipeline: Text preprocessing → Prosody → Synthesis.

  • Deep Learning-based TTS Models: Tacotron, FastSpeech, WaveNet.

  • Challenges in Speech Synthesis (Low-Resource Languages, Prosody Control).

Course Introduction 

• Lecture 1: Information Layers of SNLP

• Lecture 2: Text Normalization: Tokenization, Stemming, Lemmatization

• Lecture 3: Minimum Edit Distance I

• Lecture 4: Minimum Edit Distance II

• Lecture 5: Context-Free Grammars and Constituency Parsing

• Lecture 6:  Dependency Parsing

• Lecture 7: Sentiment Analysis

"Speech and Language Processing" by Daniel Jurafsky and James H. Martin, Prentice Hall, 2024.

"Natural Language Understanding" by James Allen, Benjamin/Cummings Publishing Company, 1987.

"Foundations of Statistical Natural Language Processing" by Christopher D. Manning and Hinrich Schütze, MIT Press, 1999.

"A Primer on Neural Network Models for Natural Language Processing" by Yoav Goldberg, Online.

"Natural Language Processing with Python" by Steven Bird, Ewan Klein, Edward Loper, O'Reilly Media, Inc., 2009.

• 2 Theoretical Assignments (14%)

• 12 Quizzes (36%)

• 1 End Term (30%)

• Classroom Notes (5%)

• Activeness in Classes (5%)

• Attendance (5%)

• X-Factor (5%)