Pronunciation Learning and Foreign Accent Reduction by an Audiovisual Feedback System

Abstract

Global integration and migration force people to learn additional languages. With respect to major languages, the acquisition is already initiated at primary school but according to their missing daily practice, many speakers keep a strong accent for longterm which may cause integration problems in new social or working environments. The possibility of later pronunciation improvements is limited since an experienced teacher and single education are required. Computer-assisted teaching methods have been established during the last decade.

Common methods do either not include a distinct user feedback (vocabulary trainer playing a reference pattern) or widely rely on fully automatic methods (speech recognition regarding the target language) causing evaluation mistakes, in particular, across the border of language groups.

The authors compiled an audiovisual database and set up an automatic system for the accent reduction (called azar) by using recordings of 11 native Russian speakers learning German and 10 native German reference speakers. The system feedback is given within a multi modal scenario.