{"id":9466,"date":"2018-08-29T17:07:36","date_gmt":"2018-08-29T17:07:36","guid":{"rendered":"https:\/\/www.inspirenignite.com\/jntuh\/?p=9466"},"modified":"2026-05-24T10:54:15","modified_gmt":"2026-05-24T05:24:15","slug":"jntuh-m-tech-2017-2018-r17-detailed-syllabus-speech-signal-processing","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/jntuh\/jntuh-m-tech-2017-2018-r17-detailed-syllabus-speech-signal-processing\/","title":{"rendered":"JNTUH M.Tech 2017-2018 (R17) Detailed Syllabus Speech Signal Processing"},"content":{"rendered":"Speech Signal Processing Detailed Syllabus for Electronics and Communication Engineering M.Tech first year first sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.\r\n\r\nThe detailed syllabus for Speech Signal Processing M.Tech 2017-2018 (R17) first year first sem is as follows.\r\n\r\nM.Tech. I Year I Sem.\r\n\r\nUNIT \u2013I : Fundamentals of Digital Speech Processing:<\/strong> Anatomy & Physiology of Speech Organs, The process of Speech Production, Acoustic Phonetics, Articulatory Phonetics, The Acoustic Theory of Speech Production- Uniform lossless tube model, effect of losses in vocal tract, effect of radiation at lips, Digital models for speech signals.\r\n\r\nUNIT \u2013II : Time Domain Models for Speech Processing<\/strong>: Introduction- Window considerations, Short time energy and average magnitude Short time average zero crossing rate, Speech Vs Silence discrimination using energy and zero crossing, Pitch period estimation using a parallel processing approach, The short time autocorrelation function, The short time average magnitude difference function, Pitch period estimation using the autocorrelation function.\r\n\r\nUNIT \u2013III : Linear Predictive Coding (LPC) Analysis:<\/strong> Basic principles of Linear Predictive Analysis: The Autocorrelation Method, The Covariance Method, Solution of LPC Equations: Cholesky Decomposition Solution for Covariance Method, Durbin\u2019s Recursive Solution for the Autocorrelation Equations, Comparison between the Methods of Solution of the LPC Analysis Equations, Applications of LPC Parameters: Pitch Detection using LPC Parameters, Formant Analysis using LPC Parameters.\r\n\r\nUNIT \u2013IV : Homomorphic Speech Processing:<\/strong> Introduction, Homomorphic Systems for Convolution: Properties of the Complex Cepstrum, Computational Considerations, The Complex Cepstrum of Speech, Pitch Detection, Formant Estimation, The Homomorphic Vocoder. Speech Enhancement: Nature of interfering sounds, Speech enhancement techniques: Single Microphone Approach : spectral subtraction, Enhancement by re-synthesis, Comb filter, Wiener filter, Multi microphone Approach\r\n\r\nUNIT-V : Automatic Speech & Speaker Recognition:<\/strong> Basic pattern recognition approaches, Parametric representation of speech, Evaluating the similarity of speech patterns, Isolated digit Recognition System, Continuous digit Recognition System. Hidden Markov Model (HMM) for Speech: Hidden Markov Model (HMM) for speech recognition, Viterbi algorithm, Training and testing using HMMS. Speaker Recognition: Recognition techniques, Features that distinguish speakers, Speaker Recognition Systems: Speaker Verification System, Speaker Identification System.\r\n\r\nTEXT BOOKS:<\/strong>\r\n