Language Proficiency Assessment Of English L2 Speakers Based On Joint Analysis Of Prosody And Native Language

In this work, we present an in-depth analysis of the interdependency between the non-native prosody and the native language (L1) of English L2 speakers, as separately investigated in the Degree of Nativeness Task and the Native Language Task of the INTERSPEECH 2015 and 2016 Computational Paralinguistics ChallengE (ComParE). To this end, we propose a multi-task learning scheme based on auxiliary attributes for jointly learning the tasks of L1 classification and prosody score regression. The effectiveness of this approach is demonstrated in extensive experimental runs, comparing various standardised feature sets of prosodic, cepstral, spectral, and voice quality descriptors, as well as automatic feature selection. In the result, we show that the prediction of both prosody score and L1 can be improved by considering both tasks in a holistic way. In particular, we achieve an 11 % relative gain in regression performance (Spearman's correlation coefficient) on prosody scores, when comparing the best multi-and single-task learning results.