Published on March 20, 2014
VOICE RECOGNITION SECURITY SYSTEMS K.SANDEEP KUMAR 10L01A0431
Point of View To implement a Voice-Recognition Security System to provide Security to your System
WHAT IS VOICE RECOGNITION SYSTEM •The term voice recognition or speaker identification refers to finding the identity of "who" is speaking, rather than what they are saying. •Recognising the Speaker can simplify the task of translating speech in systems that have been trained on a specific person's voice or it can be used to authenticate or verify the identity of a speaker as part of a security process •Personal information like Bank Password,bank amount,Criminal Data can be secured using voice Recognition
Why Voice Recognition Security Systems •Today’s password is going to be the major factor which is prone to hackers. •The password is not enough today to secure the critical Data. •These Systems can deliver bio-metric Security technology to the at low-cost than anyone else in the industry. •The system can be supportable across a wide range of platforms including the Embedded Platforms.
OBJECTIVES •Construct a Voice Recognition using Matlab. •This Construction can be done in 4 different steps. Background Math Logical Structure Hardware/Software Tradeoffs Program Design
1.Background Math •What we need to know in this projects is how to calculate the frequency to sample speech based on the Nyquist Rate Theorem. •Secondly, we also need to know how to calculate filter cutoff frequency to build the high and low pass RC filter for human speech. •Thirdly, we need to know how to calculate the gain of differential op-amp. •Lastly, we need to know how the Fourier Transform works, because we need to understand and analyze the outputs of the digital filters.
2.LOGICAL STRUCTURE The structure is very simple •The microphone circuit goes to the ADC of the MCU. The digitized sampling of the word is passed through the digital filters (flash programmed onto the MCU). •The analysis is done on the MCU as well. •. Once that is done, the LCD which is connected to the MCU displays if the word spoken matches the password or not.
3.Hardware/Software Tradeoffs •The software tradeoff in this projects is between the number of filters we can implement and the maximum number of cycles we have to adhere to. •The more filters there are, the more accurate the speech recognition will be. •However, because each filter takes about 320 cycles and we could not implement more than 2000 cycles, we had to trade off the accuracy of the system and limit the number of filters to 7.
4.Program Design •Because there is not enough memory (SRAM) on the STK500, we have to deal with speech analysis during each sample interval. •The key point of this projects is to how to design filters and how to implement them.
. There are two major difficulties we need to solve: •First reduce the running time of each filter in order to get all the finger prints before next new sample comes. So we have to use fixed- point algorithm. •Secondly, set the reasonable cutoff rate for each filter and number of stages of the filters
I.Speech Spectrum Analysis •Generally the human speech spectrum is less than 4000Hz. •. According to Nyquist theory, the minimum sampling rate for speech should be 8000samples/second. •Due to our system is voice-controlled safety system; it is very helpful to analyze the speaker's voice before our actual design.
. After we speak one word, the recorder program will store the word in a .wav file. Notice this file is sampled at 16000 samples/second, 16bit/sample, so we need to convert it into 8000samples/second, 8bits/sample. The whole analysis procedure is as the following figure
II.FingerPrint Analysis •The fingerprint of each filter is an accumulation of 250 consecutive outputs square of this filter •.Basically, different words has different frequency spectrum, then it has different fingerprint. •So we need to calculation the difference of different words and compare to the difference of same words to test whether system can recognize it.
III.Filter Design we know the frequency range of each filter. So first we use Matlab to generate their coefficients. Here we use ChebychevII filter. •Fs=4000; %Hz •Fnaq=Fs/2; % Nyquist •[B0, A0]=cheby2 (2, 20, f0); % LPF •[B6, A6]=cheby2 (2,20, f6, 'high'); % HPF •[B1, A1]=cheby2 (2, 20, [f0 f1]); % BPF
Conclusion •The project has not met expectations fully. •But Iam more happy to say that it is able to recognize a word as the password by more than 80%-90% of the time, depending on the choice of passwords. •In this case, there is a maximum of 5 words only.
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