Automated Evaluation of Non-Native English Pronunciation Quality: Combining Knowledge- and Data-Driven Features at Multiple Time Scales

Automatically evaluating pronunciation quality of non-native speech has seen tremendous success in both research and com-mercial settings, with applications in L2 learning. In this paper, submitted for the INTERSPEECH 2015 Degree of Nativeness Sub-Challenge, this problem is posed under a challenging cross-corpora setting using speech data drawn from multiple speakers from a variety of language backgrounds (L1) reading different English sentences. Since the perception of non-nativeness is re-alized at the segmental and suprasegmental linguistic levels, we explore a number of acoustic cues at multiple time scales. We experiment with both data-driven and knowledge-inspired fea-tures that capture degree of nativeness from pauses in speech, speaking rate, rhythm/stress, and goodness of phone pronunci-ation. One promising finding is that highly accurate automated assessment can be attained using a small diverse set of intuitive and interpretable features. Performance is further boosted by smoothing scores across utterances from the same speaker; our best system significantly outperforms the challenge baseline