Consonant Phoneme Based Extreme Learning Machine (ELM) Recognition Model for Foreign Accent Identification

Foreign accent automatic identification has a key role in many speech systems, such as speech recognition, speaker identification, voice conversion, and immigration screenings, etc. English speakers exhibit dialectal differences or non-native accents on specific features of their speech, and these features can be used to identify the dialect or native language of the speaker. In this paper, we proposed the consonant phoneme based Extreme Learning Machine (ELM) recognition model for accent identification based on the different pronunciation of English consonant phonemes by Arab native speakers. Mel-Frequency Cepstrum Coefficients (MFCCs) and the normalized energy parameter along with their first and second derivatives are used as acoustic features and trained with ELMs, SVMs and DBN classifiers. ELM classifier showed fast learning, and better performance, based on KFold validation with an accuracy of 88% and standard deviation (σ=0.0167), 76% by SVM and 64% with DBN classifier respectively. Our proposed ELM and SVM model showed an 11%, 16% increase in accuracy respectively over the previous work model by using the same classifier on multiple words based acoustic model to identify regional accents.