The University of Birmingham 2017 SLaTE CALL Shared Task Systems

Mengjie Qian, Xizi Wei, Peter Jancovic, Martin Russell

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper describes the system developed by the University of Birmingham for the SLaTE CALL Shared Task on grammatical and linguistic assessment of English spoken by German-speaking Swiss teenagers. Our work focused on automatic
speech recognition (ASR) but we also improved the text-processing component of the system. Several approaches to training a DNN-HMM ASR system using the AMI and the German PF-STAR corpus, plus a limited amount of Shared Task data, are described. In cross-validation evaluations on the initial Shared Task data, our final ASR system achieved a word error-rate (WER) of 9.27%, compared with 14% for the official baseline Shared Task DNN-HMM system. For text processing we expanded the baseline template-based grammar to include additional correct response patterns from the original Shared Task transcriptions. Finally, we fused the outputs of several systems at the text processing stage using linear logistic regression. Our best single and fused systems submitted to the challenge
achieved ‘D’ scores of 4.71 and 4.766, respectively, on the final test set
Original languageEnglish
Title of host publicationProceedings of the 7th ISCA Workshop on Speech and Language Technology in Education
EditorsOlov Engwall, J Lopes, I Leite
Place of PublicationStockholm, Sweden
Number of pages6
Publication statusPublished - 25 Aug 2017


  • CALL Shared Task
  • automatic speech recognition
  • text processing


Dive into the research topics of 'The University of Birmingham 2017 SLaTE CALL Shared Task Systems'. Together they form a unique fingerprint.

Cite this