Highly spectrally undersampled vowels can be classified by machines without supervision
Thayabaran Kathiresan, Dieter Maurer, Volker Dellwo · The Journal of the Acoustical Society of America · 2019
An unsupervised automatic clustering algorithm (k-means) classified 1282 Mel frequency cepstral coefficient (MFCC) representations of isolated steady-state vowel utterances from eight standard German vowel categories with fo between 196 and 698 Hz. Experiment I obtained the number of MFCCs (1–20) in connection with the spectral bandwidth (2–20 kHz) at which performance peaked (five MFCCs at 4 kHz). In experiment II, classification performance with different ranges of fo revealed that ranges with fo > 500 Hz reduced classification performance but it remained well above chance. This shows that isolated steady state vowels with strongly undersampled spectra contain sufficient acoustic information to be classified automatically.