Evaluating the Impact of Audio Segment Duration on Transformer-Based Stuttering Detection Using Wav2Vec2 - Singh, Rahul; Gupta, Deepti (International Journal of Computer Sciences and Engineering, 2025)
Stuttering is a speech disorder that disrupts the fluency of verbal communication. Traditional assessment methods are subjective and labor-intensive, prompting the need for scalable, automated solutions. Recent advances in self-supervised learning and transformer-based models such as Wav2Vec2 offer promising capabilities for automated stuttering detection. This study investigates the effect of varying audio clip lengths on the classification accuracy of stuttering using Wav2Vec2 models. Experiments were conducted on three benchmark datasets—SEP-28k, FluencyBank, and KSoF—across clip durations ranging from 3 to 11 seconds. Results show that shorter audio segments (3–5 seconds) consistently achieve better classification accuracy, with a peak of 65.13% observed for 3-second segments using SEP-28k. Longer durations introduce performance variability, especially in cross-dataset evaluations. The findings support the design of efficient, real-time stuttering detection systems and inform optimal segment length for future speech analysis models.