Language identification with language-independent acoustic models

In this paper we explore the use of language-independent acoustic models for language identiica-tion LID. The phone sequence output by a single language-independent phone recognizer is rescored with language-dependent phonotactic models approximated by phone bigrams. The language-independent phoneme inventory was obtained by Agglomerative Hierarchical Clustering, using a measure of similarity between phones. This system is compared with a parallel language-dependent phone architecture, which uses optimally the acoustic log likelihood and the phonotactic score for language identiication. Experiments were carried out on the 4-language telephone speech corpus IDEAL, containing calls in British En-glish, Spanish, French and German. Results show that the language-independent approach performs as well as the language-dependent one: 9 versus 10 of error rate on 10 second chunks, for the 4-language task.