Phonotactic spoken language identification with limited training data

Abstract

The authors investigate the addition of a new language, for which limited resources are available, to a phonotactic language identification system. Two classes of approaches are studied: in the first class, only existing phonetic recognizers are employed, whereas an additional phonetic recognizer in the new language is created for the second class. It is found that the number of acoustic recognizers employed plays a crucial role in determining the recognition accuracy for the new language. The authors study different approaches to incorporating a language for which audio-only data is available (no pronunciation dictionaries or transcriptions) and find that if more than about 2 000 training utterances are available, a bootstrapped acoustic model for the new language can improve accuracy substantially