Rhythm Metrics Predict Rhythmic Discrimination

Metrics such as VarcoV (standard deviation of vocalic interval duration divided by the mean) and %V (proportion of total utterance duration comprised of vocalic intervals) provide empirical support for long-held notions about rhythmic distinctions between languages. Furthermore, listeners can discriminate languages with distinct rhythm metric scores purely on the basis of the durational information available in resynthesized monotone sasasa speech. However, some factors contributing to this durational variation, such as stress distribution and prosodic timing, are not directly reflected in rhythm scores. To test more precisely the predictive power of rhythm metrics, we used tightly controlled sasasa stimuli, eliminating stress distribution and prosodic timing cues to focus on the information directly quantified by rhythm metrics. We show that VarcoV and %V scores are predictive of listeners’ discrimination within and between languages, even with these highly constrained stimuli.