Evaluating prosodic features for automated scoring of non-native read speech

We evaluate two types of prosodic features utilizing automatically generated stress and tone labels for non-native read speech in terms of their applicability for automated speech scoring. Both types of features have not been used in the context of automated scoring of non-native read speech to date. In our first experiment, we compute features based on a positional match between automatically identified stress and tone labels for 741 non-native read text passages with a human gold standard on the same texts read by a native speaker. Pearson correlations of up to r=0.54 between these features and human proficiency scores are observed. In our second experiment, we use stress and tone labels of the same non-native read speech corpus to compute derived features of rhythm and relative frequencies, which then again are correlated with human proficiency scores. Pearson correlations of up to r=-0.38 are observed.