Stuttering Speech Recognition System using Enhanced MFCC with Single Gated Recurrent Unit

Stuttering disrupts the natural flow of speech through repetitive or prolonged sounds, syllables, or words. Traditional identification and quantification of these disfluencies by Speech-Language Pathologists are often subjective, time-consuming, and prone to human error. This research proposes an automated method for objectively detecting stuttering using enhanced Mel Frequency Cepstral Coefficients (MFCC) combined with a Single Gated Recurrent Unit (GRU) model. Utilizing the UCLASS dataset, which includes a diverse range of stuttered speech samples, we pre-process and segment the data to extract MFCC features. These features are then input into a machine learning model, specifically a Single GRU, to classify stuttering events. The model is trained and tested with careful tuning of hyperparameters to optimize its accuracy and performance in detecting stutter in speech.