Exploring Non-Matching Multiple References for Speech Quality Assessment

Non-Matching Reference-based Speech Quality Assessment models typically require numerous references during inference to ensure stable and accurate predictions. However, this dependency introduces significant computational overhead, limiting their suitability for real-time applications. In this paper, we propose a novel training paradigm that directly addresses prediction instability at its source by integrating multiple references during training rather than during inference, as in existing approaches. This method allows the model to capture the inherent variability of reference signals, thereby enhancing prediction reliability. Additionally, we introduce an auxiliary variance loss function to minimize inconsistencies across predictions, ensuring stable assessments regardless of the number of references used. Experiments on the NISQA datasets demonstrate that, with the same training time, our method achieves consistent predictions with a single reference during inference, resulting in a 100-fold reduction in computational time while maintaining high accuracy.