SLaTE 2015 - Workshop on Speech and Language Technology in Education

Leipzig, Germany
September 4-5, 2015

Automatic Classification of Lexical Stress Errors for German CAPT

Anjana Sofia Vakil, Jürgen Trouvain

Department of Computational Linguistics & Phonetics Saarland University, Saarbrücken, Germany

Lexical stress plays an important role in the prosody of German, and presents a considerable challenge to native speakers of languages such as French who are learning German as a foreign language. These learners stand to benefit greatly from Computer-Assisted Pronunciation Training (CAPT) systems which can offer individualized corrective feedback on such errors, and reliable automatic detection of these errors is a prerequisite for developing such systems. With this motivation, this paper presents an exploration of the use of machine learning methods to classify non-native German lexical stress errors. In classification experiments using a manually-annotated corpus of German word utterances by native French speakers, the highest observed agreement between the classifier’s output and the gold-standard labels exceeded the inter-annotator agreement between humans asked to classify lexical stress errors in the same data. These results establish the viability of classification-based diagnosis of lexical stress errors for German CAPT.

Full Paper

Bibliographic reference.  Vakil, Anjana Sofia / Trouvain, Jürgen (2015): "Automatic classification of lexical stress errors for German CAPT", In SLaTE-2015, 47-52.