So let's run through an example of the process of recognizing the simple vowel "uh" as in "hood". First, we recorded four samples of the word "hood" from the four members of this group, and physically cut out the vowels using the soundeditor program. These vowels are stored in the sound files: hood_a.se.bin, hood_j.se.bin , hood_s.se.bin, and hood_t.se.bin (.au format, click to download). These files can be loaded into Matlab as a vector using the Matlab "auread" command. Most of the following graphs use only three of the above recordings for graphics purposes.We then load these sounds into Matlab. A plot of the vowels is in uh_unnormalized. We then normalize the amplitudes and center these samples. A plot of the normalized sounds is in uh_normalized. You will notice that vowels are quasi periodic. Although the pitch, or frequency of the periods, varies from speaker to speaker, the vowels each have a distinct shape that is repeated. Following the normalization, we create an Auto-Regressive Model of the voice from the sound signal. We calculate the formants from the frequency response of the AR model, and then match them to the closest standard vowel formants. In our example, all four vowels (one from each speaker) matched correctly to the vowel "uh". In uh_formants, you can see how the formants of each speakers' vowel matched with the standard formant values for "uh". The horizontal lines represent the standard formant frequencies. In uh_four_formants , we have superimposed all four formant plots for our samples. It is easiliy visible how closely the formants for different speakers match up. This plot is fairly demonstative of the similarities in the vowel speech patterns of various speakers. We have a matlab function vowelrec.m that takes a vowel sound file and the sampling frequency, and then it proceeds to carry out the steps mentioned above. It prints out a formant graph of the sound and returns a phoenetic vowel match. We conclude our demonstration by conducting the same process on a phoenetically complicated word: "syphillis". This word consists of three subtle vowels: "sif", "ful", and "lus". The audio recording of "syphillis" and its three vowels may be found in syphillis_t.se.bin, syph_1.se.bin, syph_2.se.bin, and syph_3.se.bin (all files .au format, click to download). The plot of the word "syphillis" may be found in syphillis.gif. The plot of its normalized vowels are in syph_normalized.gif. It is possible to distinguish the vowels from the consonants in the plot by the repetitive nature of the vowels. We then proceed to calculate the formants of the vowels and then do a vowel matching. Unfortunately, as our study shows, vowel matching is very successful with simple words such as "head" or "bob", but with "syphillis", our matching program was not so successful. It showed "sif" to match to AH as in bud, "ful" matched to UH as in "hood", and "lus" matched to IY as in "heed". The formants and the vowel formants they matched to are in syph_formants.gif. As is visible on the last vowel, our formant calculation missed a formant, resulting in the wide discrepency. This goes to demonstrate the fact that our program is not as effective with vowel samples that are not steady enough. Perhaps if our sampling frequency were higher than 8 KHz, we may have been more successful. However, with simpler words, our function was extremely successful. As is visible in vowels_analyzed.gif, given vowel samples from 4 different speakers, we had a correct match rate of 86%. The only vowel we were consistently unsuccessful in matching was AO as in "hawed." Nonetheless, the fact remains that none of us really know how to pronounce "hawed" anyway. In conclusion, our vowel recognition process is very successful for simple words with distinct vowels, and not successful for phonetically complicated words.