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The Vocal Joystick Vowel Corpus, by Washington University, was used to studymonophthongs pronounced by native English speakers. The objective of this studywas to quantitatively measure the extent at which speech recognition methodscan distinguish between similar sounding vowels. In particular, the phonemes/\textipa{@}/, /{\ae}/, /\textipa{A}:/ and /\textipa{2}/ were analysed. 748sound 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 TreeClassifier was used to build a predictive model that learnt the patterns of thetwo first formants measured in the data set, as well as the patterns of the 13cepstral coefficients. An accuracy of 70\% was achieved using formants for thementioned phonemes. For the MFCC analysis an accuracy of 52 \% was achieved andan accuracy of 71\% when /\textipa{@}/ was ignored. The results obtained showthat the studied algorithms are far from mimicking the ability ofdistinguishing subtle differences in sounds like human hearing does.

Pronunciation recognition of English phonemes /\textipa{@}/, /{\ae}/,  /\textipa{A}:/ and /\textipa{2}/ using Formants and Mel Frequency Cepstral  Coefficients