Spoken language recognition in the latent topic simplex

This paper proposes the use of latent topic modeling for spoken language recognition, where a topic is defined as a discrete distribution over phone n-grams. The latent topics are trained in an unsupervised manner using the latent Dirichlet allocation (LDA) technique. Language recognition is then performed in a low dimensional simplex defined by the latent topics. We apply the Bhattacharyya measure to compute the n-gram similarity in the topic simplex. Our study shows that some of the latent topics are language specific while others exhibit multilingual characteristic. Experiment conducted on the NIST 2007 language detection task shows that language cues can be sufficiently preserved in the topic simplex.