Language identification using lattice-based phonotactic and syllabotactic approaches

Recent results have shown that phone lattices may significantly improve language recognition in a PPRLM (parallel phone recognition followed by language dependent modeling) framework. In this contribution we investigate the effectiveness of lattices to language identification (LID) when using different sets of multilingual phone and syllable inventories and corresponding multilingual acoustic phone models. The LID system that achieves best results is a PPRLM structure composed of four acoustic recognizers using both phone- and syllable-based decoders. A 7-language broadcast news corpus of approximately 150 hours of speech is used for training, development and test of our LID systems. Experimental results show that the use of lattices consistently improves LID results for all multilingual acoustic model sets and for both phonotactic and syllabotactic approaches