Course Information
Course: Speech and Natural Language Processing
Prerequisites:
Course Description
Speech and Natural Language Processing deals with computational methods for understanding, representing and processing human language. The course introduces speech production, phonetics, speech recognition, linguistic analysis, morphological processing, parsing, statistical methods and machine translation.
Course Objectives
- Understand fundamental principles of speech processing.
- Study human speech production mechanism and phonetic analysis.
- Understand computational approaches for natural language processing.
- Learn techniques for morphology, syntax analysis and translation.
Course Outcomes
- Explain basic concepts of speech recognition systems.
- Understand phonetics and computational phonology.
- Apply NLP techniques including tokenization and POS tagging.
- Understand parsing methods and language models.
- Understand statistical machine translation approaches.
Course Modules
Module 1: Speech Processing
Topics Covered
- Human speech production system
- Components of speech signals
- Articulatory, acoustic and auditory phonetics
- Speech recognition systems
- Speech recognition models
- Hidden Markov Models for speech recognition
- Computational phonology
Learning Resources
Module 2: Natural Language Processing Foundations
Topics Covered
- Introduction to Natural Language Processing
- Linguistic principles and language structure
- Morphological analysis
- Finite automata in NLP
- Tokenization
- Parts-of-Speech tagging
- Statistical tagging approaches
Learning Resources
Module 3: Syntax Analysis, Parsing and Machine Translation
Topics Covered
- Syntax analysis and sentence structure
- Context-free grammar based parsing
- Parsing algorithms for natural languages
- Statistical approaches in NLP
- Statistical machine translation
- Applications of NLP in Indian languages
Learning Resources
Additional Course Resources
Applications of Speech and NLP
- Speech recognition and voice assistants
- Automatic speech-to-text conversion
- Machine translation systems
- Information retrieval and text analysis
- Natural language interfaces
Reference Topics
- Speech signal processing fundamentals
- Language modelling techniques
- Statistical approaches in NLP
- Finite automata and formal language concepts