11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Hierarchical Multilayer Perceptron Based Language Identification

David Imseng, Mathew Magimai Doss, Hervé Bourlard

Idiap Research Institute, Switzerland

Automatic language identification (LID) systems generally exploit acoustic knowledge, possibly enriched by explicit language specific phonotactic or lexical constraints. This paper investigates a new LID approach based on hierarchical multilayer perceptron (MLP) classifiers, where the first layer is a ``universal phoneme set MLP classifier''. The resulting (multilingual) phoneme posterior sequence is fed into a second MLP taking a larger temporal context into account. The second MLP can learn/exploit implicitly different types of patterns/information such as confusion between phonemes and/or phonotactics for LID. We investigate the viability of the proposed approach by comparing it against 2 standard approaches which use phonotactic and lexical constraints with the universal phoneme set MLP classifier as emission probability estimator. On SpeechDat(II) datasets of 5 European languages, the proposed approach yields significantly better performance compared to the 2 standard approaches.

Full Paper

Bibliographic reference.  Imseng, David / Doss, Mathew Magimai / Bourlard, Hervé (2010): "Hierarchical multilayer perceptron based language identification", In INTERSPEECH-2010, 2722-2725.