Abstract:
Pronunciation training is an important part of Computer Assisted Pronunciation
Training (CAPT) systems. Mispronunciation detection systems recognized pronunciation mistakes
from user’s speech and provided them feedback about their pronunciation. Acoustic phonetic features
plays a vital role in speech classification based applications. This research work investigated the
suitability of various acoustic features: pitch, energy, spectrum flux, zero-crossing, Entropy and MelFrequency Cepstral Coefficients (MFCCs). Sequential Forward Selection (SFS) was used to find out
most suitable acoustic features from the computed feature set. This study used K-Nearest Neighbors
(K-NN) classifier was used to detect the pronunciation mistakes from Arabic phonemes. This research
selected the set of most discriminative acoustic features for each phoneme. K-NN achieved accuracy of
92.15% for mispronunciation detection of Arabic Phonemes
