AI RESEARCH
Bottleneck Transformer-Based Approach for Improved Automatic STOI Score Prediction
arXiv CS.LG
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ArXi:2602.15484v2 Announce Type: replace-cross In this study, we have presented a novel approach to predict the Short-Time Objective Intelligibility (STOI) metric using a bottleneck transformer architecture. Traditional methods for calculating STOI typically requires clean reference speech, which limits their applicability in the real world. To address this, numerous deep learning-based nonintrusive speech assessment models have garnered significant interest. Many studies have achieved commendable performance, but there is room for further improvement.