Speaker Accent Recognition Using Machine Learning

Speaker Accent Recognition using Machine Learning classifies speech into six accent groups: Spanish, French, German, Italian, British English, and American English. The executed notebook uses 329 records containing twelve Mel-frequency cepstral coefficient (MFCC) features and no missing values, together with ten WAV files for demonstration. A pipeline standardises the twelve features and trains a class-balanced Random Forest classifier with 500 trees on a stratified 80:20 split. The model is evaluated on 66 samples and the notebook also extracts mean MFCC vectors from uploaded WAV files for interactive prediction. The notebook reports 83.33% accuracy, 86.80% weighted precision, 83.33% weighted recall, and 81.66% weighted F1-score. American English dominates the dataset, and French, Italian, and British English have lower recall. The result is therefore presented as an academic prototype, not as evidence of universal accent identification.