B.Tech. Degree Programme Computer Science & Engineering

B.Tech. Computer Science & Engineering (Regular)and implement digital filters to synthesize speech andcode speech at a low bit rate. Implement speechanalysis and speech synthesis modules using objectorientedsoftware programs, using techniques such asclass derivation, the use of software objects ascomponents in a larger software system.PRE-REQUISITESKnowledge of artificial intelligence, natural languageprocessing, digital signal processing, neural networks1. FUNDAMENTALS OF SPEECH RECOGNITION:Introduction, the paradigm for speech, recognition,out line, brief history of speech recognitionresearch.2. SPEECH GENERATION: Formant frequencies inspeech, parametric source-filter synthesis,formant synthesizers, pitch detection, amplitudeanalyzer, vocabulary, text-to-speech conversion,vocoders3. THE SPEECH SIGNAL: Production, reception,and acoustic-phonetic characterization: the speechproduction system, representing speech in timeand frequency domains, speech sounds andfeatures; approaches to automatic speechrecognition by machine.4. SIGNAL PROCESSING AND ANALYSISMETHODS FOR SPEECH RECOGNITION: Thebank-of filters, front-end processor; linear predictivemodel for speech recognition; vector quantization;auditory based spectral analysis model.5. PATTERN COMPARISON TECHNIQUES:Speech detection, distortion measures:mathematical considerations, distortion measuresperceptualconsiderations, spectral-distortionmeasures, incorporation of spectral dynamicfeatures into distortion measures; time alignmentand normalization.6. SPEECH RECOGNITION SYSTEM DESIGN ANDIMPLEMENTATION ISSUES: Application ofsource coding techniques to recognition, templatetraining methods; performance analysis andrecognition enhancements; template adoption tonew talkers; discriminative methods in speechrecognition; speech recognition in adverseenvironment;THEORY AND IMPLEMENTATION OF HIDDENMARKOV MODELS: Discrete time Markovprocesses; extensions to hidden Markov models;the three basic problems for HMMs; types ofHMMs; implementation issues for HMMs; HMMsystem for isolated word recognition7. SPEECH RECOGNITION BASED ONCONNECTED WORDS MODELS: Generalnotations for the connected word-recognitionproblem; two level dynamic programming algorithm;level building algorithm; one pass algorithm; multiplecandidate strings; grammar networks for connecteddigit recognition; segmental k-means trainingprocedure; connected digit recognitionimplementation; task oriented applications ofautomatic speech recognition and generation.TEXT BOOKBernard Gold and Nelson Morgan, “Speech and AudioSignal Processing”, John Wiley & Sons, 2004REFERENCE BOOKS1. Rabiner Lawrence R. and Juang B.,“Fundamentals of Speech Recognition”, PearsonEducation,20042. Rabiner Lawrence R. and Schafer R. W., “DigitalProcessing of Speech Signals”, PearsonEducation, 20043. Rabiner Lawrence R. and Bernard Gold, “Theoryand Application of Digital Signal Processing”,Prentice Hall of India, 19754. Rich Elaine and Knight Kevin, “ArtificialIntelligence”, 3rd Edition, Tata McGraw Hill, 20065. Jurafsky D. and Martin J. H., “Speech andLanguage Processing”, Pearson Education, 2009.WEB REFERENCES1. http://pages.cs.wisc.edu/~dyer/cs540/notes/speech.html2. http://www.patentstorm.us/patents/6708150/claims.html3. http://www.thefreelibrary.com/Speech+Therapy:+A+new+generation+of+voicerecognition+technology+--...-a084072940CS-437SOFT COMPUTINGL T P Cr5 0 0 3OBJECTIVETo introduce about incorporating more mathematicalapproach (beyond conventional logic system) into theartificial intelligence approaches for problem solvingsuch as fuzzy logic, genetic algorithms, etc.PRE-REQUISITESKnowledge of mathematics, statistics and probability1. INTRODUCTION: Comparison of soft computingmethods: neural networks, fuzzy logic, geneticalgorithm with conventional artificial intelligence(hard computing).2. OPTIMIZATION: Least-Square methods forsystem identification, recursive least squareestimator, LSE for nonlinear models; derivativebased optimization: descent methods, Newton’smethod, conjugate gradient methods; nonlinearleast-squares problems: Gauss Newton method,Levenberg- Marquardt method.3. NEURAL NETWORKS: Different architectures;back-propagation algorithm; hybrid learning rule,supervised learning: perceptrons, adaline, backpropagationmultilayer perceptrons, radial basisfunction networks; unsupervised learning:competitive learning network, Kohonen selforganizingnetworks, Hebbian learning, Hopfieldnetwork.4. FUZZY SET THEORY: Basic definition andterminology; basic concepts of fuzzy logic; settheoretic operators; membership functions:formulation and parameterization; fuzzy union,intersection, and complement; fuzzy rules andfuzzy reasoning; fuzzy inference systems:Mamdani and Sugeno fuzzy models; fuzzyassociative memories.5. NEURO-FUZZY MODELLING: Adaptive neurofuzzyinference systems (ANFIS), neuro-fuzzy40