ASR Evaluation

ASR evaluation studies how well speech systems turn audio into text and whether their errors matter for people and downstream tasks. Word error rate and character error rate remain common, but researchers also use language-aware normalization, semantic and human-preference measures, and task-based metrics for details such as speaker attribution, timestamps, or critical terms. Benchmarks increasingly test diverse languages and dialects, conversational overlap, clinical or other specialized speech, and acoustic shifts such as noise and reverberation. A central tension is that standardized scores enable comparisons, yet a single reference transcript or aggregate error rate can misrepresent valid linguistic variation, fairness, and practical impact.