Audio Preprocessing Effects on Stuttering Detection: A Class-Specific Analysis

Anisha Pattanayak; Hanie Kang; Huang-Cheng Chou; Sudarsana Reddy Kadiri

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arXiv · 2609.32285

Audio Preprocessing Effects on Stuttering Detection: A Class-Specific Analysis

Anisha Pattanayak·Hanie Kang·Huang-Cheng Chou·Sudarsana Reddy Kadiri

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Abstract

Audio preprocessing can affect how well a system detects stuttering. We study a simulated chain of denoising,loudness normalisation, Opus coding, and voice activity detection on SEP-28k. We use frozen WavLM Base+ features and report pointwise confidence intervals from episode-level bootstrap resampling. At a fixed threshold of 0.5, the chain reduces block F1 from 0.638 to 0.465, with smaller decreases for the other four classes. ROC-AUC decreases for all five classes. Blocks show the largest F1 and ROC-AUC losses, while sound repetitions show the largest average precision loss. Tuning the threshold on processed validation audio raises block F1 to 0.630. Retraining on processed audio with threshold tuning gives 0.628. Threshold adjustment therefore accounts for most of the observed block F1 recovery. It does not change ROC-AUC, which retraining raises only from 0.620 to 0.633, compared with 0.724 on clean audio.

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Anisha Pattanayak, Hanie Kang, Huang-Cheng Chou, Sudarsana Reddy Kadiri. 2026-09-26. Audio Preprocessing Effects on Stuttering Detection: A Class-Specific Analysis. https://arxiv.org/abs/2609.32285

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