Pronunciation recognition of English phonemes /\textipa@/, /æ/, /\textipaA:/ and /\textipa2/ using Formants and Mel Frequency Cepstral Coefficients
Table 2: Formants measurement in Hertz. Our measurements are compared with Hawkings et. al., Hunter et. al., Deterding.
['Study Phoneme', 'Our  [ITALIC] F1', 'Our  [ITALIC] F2', 'Hawkings  [ITALIC] et. al.  [ITALIC] F1', 'Hawkings  [ITALIC] et. al.  [ITALIC] F2', 'Hunter  [ITALIC] et. al.  [ITALIC] F1', 'Hunter  [ITALIC] et. al.  [ITALIC] F2', 'Deterding  [ITALIC] F1', 'Deterding  [ITALIC] F2']
This is supported by observations in which it was seen that formants do not only depend on the geometry of the vocal tract of each person, but also are statistically dependent on the age, as shown in (Hawkings & Midgley, 2005); as well as on gender, as shown in (Hillenbrand, Getty, Clark, & Wheeler, 1995). Therefore, we see that identifying a phoneme by its formants is not trivial, but as we showed, can be done with an estimated accuracy of 70%. Despite the low accuracy, formants keep being used for vowel pronunciation research, as in (Rasilo & Räsänen, 2017), in which the process of infants learning their native language was simulated through a Leaning Virtual Infant that received sounds from its caregivers. At first the Learning Virtual Infant babbled randomly, but as the learning process advanced, it began to compute formants to identify similar sounding vowels and to improve its pronunciation.