Tokenizing fundamental frequency variation for Mandarin tone error detection

Tone error is commonly observed in tonal language acquisition. Correct tone production is especially challenging for native speakers of non-tonal languages. In this paper, we exploit the fundamental frequency variation (FFV) feature for Mandarin tone error detection. We propose to use FFV through two approaches: (1) Concatenating FFVs along side with standard speech recognition features; (2) Token FFV: Characterizing pitch variation with longer temporal context through GMM tokenization and n-gram language modeling. Our results show that tone error detection improves by incorporating FFV features and the two approaches are complementary to each other.