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The coding has been developed using MFCC and VQ algorithm, in MATLAB 7.5 version on window Vista platform and supporting hardware also has _ been implemented. For example the speech databases consist of 10 speeches, here the Fig. 8, shows the speech added to data base and Fig. 9, shows the recognized speaker ID 2. Fig 6. Basic speech recognition block diagram

Figure 8 The coding has been developed using MFCC and VQ algorithm, in MATLAB 7.5 version on window Vista platform and supporting hardware also has _ been implemented. For example the speech databases consist of 10 speeches, here the Fig. 8, shows the speech added to data base and Fig. 9, shows the recognized speaker ID 2. Fig 6. Basic speech recognition block diagram

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Fig 1. Block diagram of MFCC processor The following formula can used to compute the mels for given frequency f, in Hz. [8] The output of the equation (1) is log mel spectrum, it has to be converted back into time. The result is called the mel frequency cepstrum coefficients (MFCCs). This may be converted to the time domain using the Discrete Cosine Transform (DCT). The MFCCs may be calculated using this equation. [6,8] measured in Hz, a subjective pitch is measured on the ‘Mel’ scale. The mel-frequency scale is linear frequency spacing below 1000Hz and a logarithmic spacing above 1000Hz. As a reference point, the pitch of a 1 kHz tone, 40dB above the perceptual hearing threshold, is defined as 1000 mels.
Fig 1. Block diagram of MFCC processor The following formula can used to compute the mels for given frequency f, in Hz. [8] The output of the equation (1) is log mel spectrum, it has to be converted back into time. The result is called the mel frequency cepstrum coefficients (MFCCs). This may be converted to the time domain using the Discrete Cosine Transform (DCT). The MFCCs may be calculated using this equation. [6,8] measured in Hz, a subjective pitch is measured on the ‘Mel’ scale. The mel-frequency scale is linear frequency spacing below 1000Hz and a logarithmic spacing above 1000Hz. As a reference point, the pitch of a 1 kHz tone, 40dB above the perceptual hearing threshold, is defined as 1000 mels.
The LBG VQ design algorithm is an iterative algorithm. It is proposed by Y. Linde, A. Buzo & R. Gray. This alternatively solves optimality criteria [10]. The algorithm requi codebook is obtained method, an initial cod res an initial codebook. The initial by the splitting method. In this evector is set as the average of the entire training sequence. This codevector is then split into two. The iterative algorithm is run with these two vectors as the initial codebook. T four and the process is he final two codevectors are split into repeated until the desired number of codevectors is obtained. The algorithm is summarized in the
The LBG VQ design algorithm is an iterative algorithm. It is proposed by Y. Linde, A. Buzo & R. Gray. This alternatively solves optimality criteria [10]. The algorithm requi codebook is obtained method, an initial cod res an initial codebook. The initial by the splitting method. In this evector is set as the average of the entire training sequence. This codevector is then split into two. The iterative algorithm is run with these two vectors as the initial codebook. T four and the process is he final two codevectors are split into repeated until the desired number of codevectors is obtained. The algorithm is summarized in the
B. LBG Design Algorithm
B. LBG Design Algorithm
Fig 4. conceptual diagram that explains VQ process flowchart of Fig. 3. In Fig. 4, the VQ is shown for two speakers. The circle refers the speaker | and triangle refers for speakers 2. [5] In the training phase, a speaker-specific VQ codebook is generated for each known speaker. Fig. 5, shows the use of different number of centroids for the same data field. After calculation of MFCC and VQ, Euclidian distance is calculated for nearest speech matching.
Fig 4. conceptual diagram that explains VQ process flowchart of Fig. 3. In Fig. 4, the VQ is shown for two speakers. The circle refers the speaker | and triangle refers for speakers 2. [5] In the training phase, a speaker-specific VQ codebook is generated for each known speaker. Fig. 5, shows the use of different number of centroids for the same data field. After calculation of MFCC and VQ, Euclidian distance is calculated for nearest speech matching.
Fig 5. Pictorial view of codebook with 15 Centroids respectively. In the experimental setup of DC motor drive through speech recognition, the speech signal is taken by microphone that is connected to computer. Software coding is to calculate the MFCC and VQ (LBG algorithm) MATLAB 7.5 version is used here in this paper, to recognize the input speech taken from micro phone. And for hardware part to make DC motor understands, microcontroller (8051) is used. For microcontroller coding Embedded C programming is used. The interfacing between computer and microcontroller is done by RS-232. For drive the DC motor the driver IC L293D is used. The Fig. 6 shows the basic speech recognition block diagram.
Fig 5. Pictorial view of codebook with 15 Centroids respectively. In the experimental setup of DC motor drive through speech recognition, the speech signal is taken by microphone that is connected to computer. Software coding is to calculate the MFCC and VQ (LBG algorithm) MATLAB 7.5 version is used here in this paper, to recognize the input speech taken from micro phone. And for hardware part to make DC motor understands, microcontroller (8051) is used. For microcontroller coding Embedded C programming is used. The interfacing between computer and microcontroller is done by RS-232. For drive the DC motor the driver IC L293D is used. The Fig. 6 shows the basic speech recognition block diagram.
Fig 8. Sound added to database in MATLAB
Fig 8. Sound added to database in MATLAB
Fig 9. Recognized speaker ID 2 Fig 10. Graph of speaker voice database
Fig 9. Recognized speaker ID 2 Fig 10. Graph of speaker voice database
Fig 11. Graph for hamming window
Fig 11. Graph for hamming window
Fig 12. Hamming window multiplied by input signal Fig 13. Applied filter banks
Fig 12. Hamming window multiplied by input signal Fig 13. Applied filter banks
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