Joseph Tepperman, Theban Stanley, Kadri Hacioglu, and Bryan Pellom, Rosetta Stone Labs
Parroting exercises in a foreign language are designed to make a student's speech more native-like through imitation of specific native speech templates. In this paper we describe novel template-based methods for automatically estimating subjective scores for both intonation and rhythm in nonnative English. In terms of accuracy when automatically classifying a parroting speaker as a native or a learner, experimental results show that these new rhythm and intonation scores outperform similar baselines from nonnative speech assessment literature, and that they offer complementary discriminatory information when combined with automatic segment-level pronunciation scores, reaching a maximum classification accuracy of 89.8\% on a corpus of parroting exercises. This suggests the general usefulness of these new scores in automatically assessing nonnative pronunciation in a computer-assisted pronunciation practice scenario.