VOICE RECOGNITION SECURITY SYSTEM

VOICE RECOGNITION SECURITY SYSTEM
Extended Abstract
When we think of programmable speech recognition, we think of calling FedEx
customer service call center with automated voice recognition response
systems. We also think of PC-based speech recognition Dragon
NaturallySpeaking. Now we took that a step further. We are talking about
speech recognition in a tiny Mega32 microcontroller. We are talking about real-
time speech processing which means there is no need to store the samples in
an external memory at all. This was made possible by implementing bandpass
filters in assembly language with fixed-point format onto the microcontroller. In
this filter design, not only the output of the filter is calculated, but its square and
accumulation also obtained. Thus much time is saved so that each speech
sample can be processed to get its frequency spectrum before next new
sample comes. In addition, the analysis of the voice is made using correlation
and regression method to compare the voiceprint of different words. These
techniques provide stronger ability to recognize the same word. Training
procedure is also used to reduce the random changes due to one word is
spoken different times. The training procedure can get the more accurate
frequency spectrum for one word. The experimental results demonstrate high
accuracy for this real-time speech recognition system.

Introduction
Description of Project
The function of this speech recognition security system is to have a system
that will only unlock upon recognizing a voice password spoken by the
administrator or password holder.

Summary
Firstly, we looked at the speech recognition algorithm to understand the
implementation. We then prepared the microphone circuit, and then proceeded
to start sampling and generate the digital data for the speech. Once we have
the data, we started writing the code based on Tor's speech recognition
algorithm. We also wrote the digital filters in assembly code to save the
number of cycles necessary for the sampling rate of the speech, which is at
4K/second. Afterwards, we analyzed the output of the filters to recognize
which word was spoken. Finally, we added an LCD for better user interface to
signal if the password spoken is correct or not.


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