Speech Processing Telecom 8th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective -5)

Speech Processing detail syllabus for Telecommunication Engineering (Telecom), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17EC832), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.

For all other telecom 8th sem syllabus for be 2017 scheme vtu you can visit Telecom 8th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective -5 subjects do refer to Professional Elective -5. The detail syllabus for speech processing is as follows.

Course Objectives:

This course enables students to:

• Introduce the models for speech production
• Develop time and frequency domain techniques for estimating speech parameters
• Introduce a predictive technique for speech compression
• Provide fundamental knowledge required to understand and analyse speech recognition, synthesis and speaker identification systems.

Module 1
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Module 2

Time-Domain Methods for Speech Processing: Introduction to Short-Time Analysis of Speech, Short-Time Energy and Short-Time Magnitude, Short-Time Zero-Crossing Rate, The Short-Time Autocorrelation Function, The Modified Short-Time Autocorrelation Function, The Short-Time Average Magnitude Difference Function.

Module 3

Frequency Domain Representations: Discrete-Time Fourier Analysis, Short-Time Fourier Analysis, Spectrographic Displays, Overlap Addition(OLA),Method of Synthesis, Filter Bank Summation(FBS) Method of Synthesis, Time-Decimated Filter Banks, Two-Channel Filter Banks, Implementation of the FBS Method Using the FFT, OLA Revisited, Modifications of the STFT.

Module 4
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Module 5

Linear Predictive Analysis of Speech Signals: Basic Principles of Linear Predictive Analysis, Computation of the Gain for the Model, Frequency Domain Interpretations of Linear Predictive Analysis, Solution of the LPC Equations, The Prediction Error Signal, Some Properties of the LPC Polynomial A(z), Relation of Linear Predictive Analysis to Lossless Tube Models, Alternative Representations of the LP Parameters.

Course Outcomes:

Upon completion of the course, students will be able to:

• Model speech production system and describe the fundamentals of speech.
• Extract and compare different speech parameters.
• Choose an appropriate speech model for a given application.
• Analyse speech recognition, synthesis and speaker identification systems

Text Books:

Theory and Applications of Digital Speech Processing-Rabiner and Schafer, Pearson Education 2011

Reference Books:

1. Fundamentals of Speech Recognition- Lawrence Rabiner and Biing-Hwang Juang, Pearson Education, 2003.
2. Speech and Language Processing-An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition- Daniel Jurafsky and James H Martin, Pearson Prentice Hall 2009.

For detail syllabus of all other subjects of BE Telecom, 2017 regulation do visit Telecom 8th Sem syllabus for 2017 Regulation.

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