SLaTE 2015 - Workshop on Speech and Language Technology in Education

Leipzig, Germany
September 4-5, 2015

An Improved DNN-based Approach to Mispronunciation Detection and Diagnosis of L2 Learners’ Speech

Wenping Hu (1,2), Yao Qian (2), Frank K. Soong (2)

(1) University of Science and Technology of China, Hefei, China
(2) Microsoft Research, Beijing, China

We extend the Goodness of Pronunciation (GOP) algorithm from the conventional GMM-HMM to DNNHMM and further optimize the GOP measure toward L2 language learners’ accented speech. We evaluate the performance of the new proposed approach at phone-level mispronunciation detection and diagnosis on an L2 English learners’ corpus. Experimental results show that the Equal Error Rate (EER) is improved from 32.9% to 27.0% by extending GOP from GMM-HMM to DNN-HMM and the EER can be further improved by another 1.5% to 25.5% with our optimized GOP measure. For phone mispronunciation diagnosis, by applying our optimized DNN based GOP measure, the top-1 error rate is reduced from 61.0% to 51.4 %, compared with the original DNN based one, and the top-5 error rate is reduced from 8.4% to 5.2 %. On a continuously read, L2 Mandarin learners’ corpus, our approaches also achieve similar improvements.

Full Paper

Bibliographic reference.  Hu, Wenping / Qian, Yao / Soong, Frank K. (2015): "An improved DNN-based approach to mispronunciation detection and diagnosis of L2 learners’ speech", In SLaTE-2015, 71-76.