  The Vocal Joystick Vowel Corpus, by Washington University, was used to study
monophthongs pronounced by native English speakers. The objective of this study
was to quantitatively measure the extent at which speech recognition methods
can distinguish between similar sounding vowels. In particular, the phonemes
/\textipa{@}/, /{\ae}/, /\textipa{A}:/ and /\textipa{2}/ were analysed. 748
sound files from the corpus were used and subjected to Linear Predictive Coding
(LPC) to compute their formants, and to Mel Frequency Cepstral Coefficients
(MFCC) algorithm, to compute the cepstral coefficients. A Decision Tree
Classifier was used to build a predictive model that learnt the patterns of the
two first formants measured in the data set, as well as the patterns of the 13
cepstral coefficients. An accuracy of 70\% was achieved using formants for the
mentioned phonemes. For the MFCC analysis an accuracy of 52 \% was achieved and
an accuracy of 71\% when /\textipa{@}/ was ignored. The results obtained show
that the studied algorithms are far from mimicking the ability of
distinguishing subtle differences in sounds like human hearing does.
