Natural langauge & speech processing
The course of Natural Language & Speech Processing (NLSP) has pre-requites: Data Structures, Algorithms, Discrete Mathematical structures, and Theory of Computation
The objective the NLSP course is to understand the basic principles of Speech Processing, as well as Natural Language Processing. The Speech Processing is concerned with conversion of human speech to text form. The course of speech processing covers: Phonetics, compiler design, its Human speech production system, phonemes, syllables, and words. Various probabilistic models are used for realization of conversion from voice signals to text. This includes, Bayesian probability, and Markov chains. The course also covers tools and programming, that includes programming and speech Engines.
The Natural language Processing(NLP) is concerned with recognition and representation of Natural language texts. The course covers in details about tokenization, parsing, various representation and inference techniques.
Number of applications, and their implementation aspects of NLP cover: Information Retrieval, Machine Translations, TTS (text to speech Translation). The other objective is, how to construct efficient algorithms for all these.
The outcome of the course of Speech processing is to acquire basic understanding about phonetics, IPA, phonemes, syllabus, and structure of words; also about, how the human speech is generated. On completion of the course, the students will be able to test and implement various algorithms and techniques using simulations tools and Speech Engines.
As part of NLP, the students will gain understanding about what are the techniques and tools for NLP, and shall be conversant with NLTK and Python language based applications for Natural language processing.
Module 1: speech processing
Phonetics is an important part of speech processing. It covers articulation phonetics, acoustical phonetics, and auditory phonetics. The phonetic part covers, pronunciation and its rules, and how the speech sound is generated.
Module 2: natural language processing
Natural language processing covers basic theory of linguistics, lexical analysis, syntax analysis, parts-of-tagging, parsing, pragmatics, and discourse analysis.
Among the major challenges of NLP is ambiguity in the Natural Language. Various ambiguities are: lexical ambiguity, semantic ambiguity, and ambiguity in understanding the discourse
Among the major applications of NLP are: Information Retrieval, Information Extraction, Question-Answering, Machine Translation, and text-summarization