Speech Processing It 8th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective V) detail syllabus for Information Technology (It), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (IT8077), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other it 8th sem syllabus for be 2017 regulation anna univ you can visit It 8th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective V subjects do refer to Professional Elective V. The detail syllabus for speech processing is as follows.
Course Objective:
- To understand the fundamentals of the speech processing
- Explore the various speech models
- Gather knowledge about the phonetics and pronunciation processing
- Perform wavelet analysis of speech
- To understand the concepts of speech recognition
Unit I
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Unit II
Speech Modelling
Word classes and part of speech tagging – hidden markov model – computing likelihood: the forward algorithm – training hidden markov model – maximum entropy model -transformation-based tagging – evaluation and error analysis – issues in part of speech tagging – noisy channel model for spelling
Unit III
Speech Pronunciation and Signal Processing
Phonetics – speech sounds and phonetic transcription – articulatory phonetics -phonological categories and pronunciation variation – acoustic phonetics and signals -phonetic resources – articulatory and gestural phonology
Unit IV
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Unit V
Speech Recognition
Automatic speech recognition – architecture – applying hidden markov model – feature
extraction: mfcc vectors – computing acoustic likelihoods – search and decoding -embedded training – multipass decoding: n-best lists and lattices- a* (stack) decoding -context-dependent acoustic models: triphones – discriminative training – speech recognition by humans
Course Outcome:
On Successful completion of the course ,Students will be able to
- Create new algorithms with speech processing
- Derive new speech models
- Perform various language phonetic analysis
- Create a new speech identification system
- Generate a new speech recognition system
Text Books:
- Daniel Jurafsky and James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Person education,2013.
References:
- Kai-Fu Lee, Automatic Speech Recognition, The Springer International Series in Engineering and Computer Science, 1999.
- Himanshu Chaurasiya, Soft Computing Implementation of Automatic Speech Recognition, LAP Lambert Academic Publishing, 2010.
- Claudio Becchetti, Klucio Prina Ricotti, Speech Recognition: Theory and C++ implementation,Wiley publications 2008.
- Ikrami Eldirawy , Wesam Ashour, Visual Speech Recognition, Wiley publications , 2011
For detail syllabus of all other subjects of BE It, 2017 regulation do visit It 8th Sem syllabus for 2017 Regulation.
Dont forget to download iStudy for latest syllabus and results, class timetable and more.