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21Lecture 21 : Introduction to Liner PredictionDownload
22Lecture 22 : Autocorrelation Method of LPC analysisDownload
23Lecture 23 : Autocorrelation Method of LPC analysis ( Contd.)Download
24Lecture 24 : Lattice Formulations of Linear Prediction Download
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31Lecture 31: Segmental and Supra-segmental features of speech signalDownload
32Lecture 32: Cepstral Transform Coefficients (CC) Parameters extractionDownload
33Lecture 33: Mel Frequency Cepstral CoefficientsDownload
34Lecture 34: MFCC features vectorDownload
35Lecture 35: Fundamental Frequency (F0) Detection of speech signalDownload
36Lecture 36: Frequency Domain Fundamental Frequency Detection AlgorithmsDownload
37Lecture 37 Text to Speech SynthesisDownload
38Lecture 38 Text to Speech Synthesis ( Contd.)Download
39Lecture 39 Automatic Speech RecognitionDownload
40Lecture 40 Statistical Modeling of Automatic Speech RecognitionDownload
41Lecture 41 Speech based Technology Development for e-learningDownload
42Lecture 42 : Prosody ModelingDownload
43Lecture 43 : Fundamental frequency countur modelingDownload
44Lecture 44 : Fundamental frequency contour modeling (Contd.)Download

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19Lecture 19: Time Domain Methods in Speech Processing Download
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24Lecture 24 : Lattice Formulations of Linear Prediction Download
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31Lecture 31: Segmental and Supra-segmental features of speech signalDownload
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32Lecture 32: Cepstral Transform Coefficients (CC) Parameters extractionDownload
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33Lecture 33: Mel Frequency Cepstral CoefficientsDownload
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34Lecture 34: MFCC features vectorDownload
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35Lecture 35: Fundamental Frequency (F0) Detection of speech signalDownload
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36Lecture 36: Frequency Domain Fundamental Frequency Detection AlgorithmsDownload
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37Lecture 37 Text to Speech SynthesisDownload
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38Lecture 38 Text to Speech Synthesis ( Contd.)Download
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39Lecture 39 Automatic Speech RecognitionDownload
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40Lecture 40 Statistical Modeling of Automatic Speech RecognitionDownload
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41Lecture 41 Speech based Technology Development for e-learningDownload
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42Lecture 42 : Prosody ModelingDownload
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