Voice decoder for vowels Using Atmega644

Then they would turn on the LED that corresponded to the most energetic frequency division in the input frequency spectrum. This made us wonder if identifying speech is possibly by a method similar to this.
In fact, with today’s technology, speech recognition is fully realizable and can even be fully synthesized. However, most of the software that deals with speech recognition require extensive computation and are very expensive. With the limited computation power of mega644 and a $75 project budget, we wanted to make a simple, smart voice recognition system that is capable of recognizing simple vowels.
After careful research and several discussions with Bruce, we found that vowels can be characterized by 3 distinct peaks in their frequency spectrum. This means if we perform a transform to input speech signal, the frequency spectrum profile will contain characteristic peaks that correspond to the most energetic frequency component. Then if we check to see if the 3 peaks in the input fall in the ranges we defined for a specific vowel, we will be able to deduce is that vowel component was present or not in the user’s speech.

The main structure of our decoder system centers on the mega644 MCU. Our program allows the MCU to coordinate commands being placed by the user via PuTTY and the button panel while analyzing the user’s audio input in real time. On the lowest design level (hardware), we have microphone and a button panel to convert physical inputs by the user into analog and digital signals the MCU can react to. On the highest level, PuTTY displays the operation status of the MCU and informs the MCU of user commands being placed at the command line. PuTTY also offers user the freedom to test the accuracy of our recognition and simulates a security system where the user must say a specific sequence of vowels to see a secret message.

Basically, the first three formant frequencies (refer to peaks in harmonic spectrum of a complex sound) can attribute to the different appeal of vowel sounds.
Therefore, if we can pick out formant by intensities in different frequency ranges, we can identify a vowel sound and use sequence of vowel to generate an audio pass code specific to that vowel.

In this part, most research we did were based on common vowel characters like ‘A’,’E’,’I’,’O’,’U’, which demonstrated that the method we attempt to develop could achieve. Yet in the real case, we found that the difference of these five characters is not as obvious as simply comparison between frequency sequency could distinguish.
We first use Adobe Audition to observe initial input waveform taken directly from Microphone and AT&T text2speech as shown in the picture. Although the waveform corresponding to the same vowel would result in a similar shape, there still exists difference which we may find more straightforward in frequency domain.
The first program in MATLAB is based on Prof. Land’s code that compares the FFT and FWT outputs as spectrograms, then takes the maximum of each time sliced transform and compares these spectrograms. Top row is FFT power spectrum, FWT sequency spectrum is in the bottom. The maximum intensity coefficient of each spectrogram time slice in FFT and FWT are almost in the same shape. We’ll take one spectrum as an example.
Another program directly implements FFT and show a frequency series. In this figure we can clearly see the resonance peaks of a vowel. This transform is 256 points. Also, notice that because of noise interference, it would be hard to tell apart the second peak for [EE] and this is not the only case.