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LIST OF FIGURES

Figure 2.1: Speech Recognition technology in modern cars . . . 4

Figure 2.2: Two robots controlled by speech . . . 4

Figure 2.3: Speech password device . . . 5

Figure 2.4: Speech Recognition feature in Windows 8 . . . 5

Figure 2.5: Speech signal for the word “one” . . . 7

Figure 2.6: Human speech production apparatus . . . . . . 8

Figure 3.1: General structure of Speech Recognition system . . . 12

Figure 3.2: Sampling process . . . .. . . 13

Figure 3.3: Framing process . . . 15

Figure 3.4: Block diagram of LPC estimation . . . 17

Figure 3.5: Block diagram of LPC analysis . . . 18

Figure3.6: Block diagram of LPC synthesis . . . 19

Figure3.7: Mel scale filter bank . . . 20

Figure3.8: Mel scale plot . . . . . . 20

Figure3.9: Block diagram of MFCC . . . 21

Figure3.10: Spectrogram of wideband speech signal . . . . . . 23

Figure3.11: Spectrogram of narrowband speech signal . . . . . . 24

Figure3.12: Exchange the axis of the frame . . . . . . 24

Figure3.13: Steps to get Spectrogram . . . 25

Figure 4.1: General structure of neural network . . . 27

Figure 4.2: Neural network topologies . . . 28

Figure 4.3: Mathematical neuron . . . . . . 30

Figure 4.4: Linear function . . . .. . . 31

Figure 4.5: Binary function . . . 31

Figure 4.6: Sigmoid function . . . 32

Figure 4.7: Fully connected Feed-Forward neural network. . . .. . . 33

Figure 5.1: General structure of the program . . . 36

Figure 5.2: Flowchart of LPC method . . . 38

Figure 5.3: Flowchart of MFCC method . . . 39

Figure 5.4: Flowchart of Spectrogram method . . . 40

Figure 5.5: Operation of end points detection (a) Source signal, (b) End points detected signal. 41

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Figure 5.6: A one frame of the word “one” . . . 41

Figure 5.7: Frame signal after applying hamming window. . . 42

Figure 5.8: LPC coefficients for a one frame . . . 47

Figure 5.9: LPC coefficients for a word “one” . . . 47

Figure 5.10: Spectrum of a one windowed frame . . . 48

Figure 5.11: Mel filters bank . . . 49

Figure 5.12: MFCC coefficients for a one frame . . . 49

Figure 5.13: MFCC coefficients for the word “one” . . . 50

Figure 5.14: Obtained signal after rotating spectrum of the windowed frame . . . 51

Figure 5.15: Spectrogram of the word “one” . . . 51

Figure5.16: Speech Recognition system . . . .. . . 53

Figure 5.17: Hamming window for a one frame of the word “one” . . . 59

Figure 5.18: Hamming window of the word “one” .. . . 59

Figure 5.19: LPC coefficients for “one” . . . 60

Figure5.20: How features enter to the neural network and how output is gotten . . . 62

Figure5.21: Neural network applied window . . . . . . 64

Figure5.22: First display of displaying interface window. . . .. . . 65

Figure 5.23: Select a type of recognizing window . . . .. . . 66

Figure5.25: Selecting a not trained word window . . . 67

Figure5.25: Decision window for a trained word . . . .. . . 67

Figure 5.26: Plotting of the matched trained word . . . .. . . 68

Figure5.27: Selecting a not trained word window . . . 69

Figure 5.28: Displaying new buttons in main window . . . . . . 69

Figure 5.29: Speech signal after adding noise . . . 71

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