Speech Quality Assessment

Speech quality assessment studies how listeners perceive speech and how reliably systems can measure qualities such as naturalness, intelligibility, pronunciation, prosody, and speaker similarity. Methods range from human mean-opinion and pairwise-preference ratings to reference-based metrics and non-intrusive predictors built from acoustic features, self-supervised speech representations, or audio-language models. Researchers also develop targeted assessments for settings such as speech enhancement, text-to-speech, accents, and pathological speech, often using benchmarks that test multiple dimensions and domains. A central challenge is that high metric scores do not always match human judgments or downstream usefulness, especially when systems encounter new speakers, languages, or speaking styles.