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Multilingual spoken dialogue systems have gained prominence in the recentpast necessitating the requirement for a front-end Language Identification(LID) system. Most of the existing LID systems rely on modeling the languagediscriminative information from low-level acoustic features. Due to thevariabilities of speech (speaker and emotional variabilities, etc.),large-scale LID systems developed using low-level acoustic features suffer froma degradation in the performance. In this approach, we have attempted to modelthe higher level language discriminative phonotactic information for developingan LID system. In this paper, the input speech signal is tokenized to phonesequences by using a language independent phone recognizer. The languagediscriminative phonotactic information in the obtained phone sequences aremodeled using statistical and recurrent neural network based language modelingapproaches. As this approach, relies on higher level phonotactical informationit is more robust to variabilities of speech. Proposed approach iscomputationally light weight, highly scalable and it can be used in complementwith the existing LID systems.

A language model based approach towards large scale and lightweight language identification systems