Behavioral Pattern Analysis between Bilingual and Monolingual Listeners’ Natural Speech Perception on Foreign-Accented English Language Using Different Machine Learning Approaches

Speech perception in an adverse background/noisy environment is a complex and challenging human process, which is made even more complicated in foreign-accented language for bilingual and monolingual individuals. Listeners who have difficulties in hearing are affected most by such a situation. Despite considerable efforts, the increase in speech intelligibility in noise remains elusive. Considering this opportunity, this study investigates Bengali–English bilinguals and native American English monolinguals’ behavioral patterns on foreign-accented English language considering bubble noise, gaussian or white noise, and quiet sound level. Twelve regular hearing participants (Six Bengali–English bilinguals and Six Native American English monolinguals) joined in this study. Statistical computation shows that speech with different noise has a significant effect (p = 0.009) on listening for both bilingual and monolingual under different sound levels (e.g., 55 dB, 65 dB, and 75 dB). Here, six different machine learning approaches (Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbors (KNN), Naïve Bayes (NB), Classification and regression trees (CART), and Support vector machine (SVM)) are tested and evaluated to differentiate between bilingual and monolingual individuals from their behavioral patterns in both noisy and quiet environments. Results show that most optimal performances were observed using LDA by successfully differentiating between bilingual and monolingual 60% of the time. A deep neural network-based model is proposed to improve this measure further and achieved an accuracy of nearly 100% in successfully differentiating between bilingual and monolingual individuals.