  Multilingual spoken dialogue systems have gained prominence in the recent
past necessitating the requirement for a front-end Language Identification
(LID) system. Most of the existing LID systems rely on modeling the language
discriminative information from low-level acoustic features. Due to the
variabilities of speech (speaker and emotional variabilities, etc.),
large-scale LID systems developed using low-level acoustic features suffer from
a degradation in the performance. In this approach, we have attempted to model
the higher level language discriminative phonotactic information for developing
an LID system. In this paper, the input speech signal is tokenized to phone
sequences by using a language independent phone recognizer. The language
discriminative phonotactic information in the obtained phone sequences are
modeled using statistical and recurrent neural network based language modeling
approaches. As this approach, relies on higher level phonotactical information
it is more robust to variabilities of speech. Proposed approach is
computationally light weight, highly scalable and it can be used in complement
with the existing LID systems.
