PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets

Researchers introduce PrefSQA, a machine learning method that predicts speech quality through pairwise preference comparisons rather than traditional mean opinion scores (MOS). The approach incorporates uncertainty-aware logits and attention mechanisms, demonstrating that preference-based labeling produces cleaner, more reliable datasets than scalar MOS ratings, though improvements vary significantly based on dataset quality.

Speech quality assessment has traditionally relied on mean opinion scores, where listeners assign numerical ratings to audio samples. This scalar approach suffers from inherent limitations: rater variability, subjective interpretation differences, and inconsistent listening conditions introduce labeling noise that degrades model reliability. PrefSQA addresses this fundamental problem by shifting the evaluation paradigm toward direct pairwise comparisons, where listeners simply indicate which of two samples sounds better. This comparative approach naturally reduces subjective variance because humans excel at relative judgments more than absolute ratings.

The research validates a broader trend in machine learning toward preference-based learning over scalar labels. By employing uncertainty-aware logits and impairment attention mechanisms, PrefSQA captures nuanced quality differences while accounting for prediction confidence. The team's systematic evaluation across five datasets—including MOS-derived sets, low-noise simulated comparisons, and human preference data—reveals a critical insight: dataset quality matters more than methodological sophistication. Results show marginal gains on noisy MOS-derived data but substantial improvements on high-quality preference sets.

For the speech processing industry, this work establishes preference prediction as a viable alternative for quality assessment in applications ranging from voice coding to audio enhancement. The methodology has downstream implications for training robust speech models with cleaner supervision signals. The research underscores that practitioners cannot simply port existing datasets into new frameworks; rather, collecting or curating preference data specifically for target applications yields meaningfully better results. Organizations developing speech technologies should consider preference-based evaluation protocols, particularly when dealing with applications where rater consistency has historically caused issues.

• →Pairwise preference comparisons produce cleaner training labels than scalar mean opinion scores by leveraging humans' superior relative judgment abilities.
• →Dataset quality significantly outweighs algorithmic improvements; high-quality preference data yields substantial gains while MOS-derived data shows minimal improvement.
• →PrefSQA incorporates uncertainty awareness and impairment attention to better capture audio quality nuances across matching and non-matching reference comparisons.
• →Preference-based evaluation protocols offer a practical alternative for speech quality assessment in production applications where labeling noise has been problematic.
• →The research validates that supervised learning frameworks benefit from domain-specific data collection strategies rather than repurposing legacy datasets.