Automatic Language Identification using Suprasegmental Feature and Supervised Topic Model

Abstract

Language identification is quite challenging when it comes to discriminating between closely related dialects of the same language. The fundamental issue is to explore the discriminative cue and effective representation. In this paper, the multi-dimensional language cues are used to distinguish languages, which includes the phonotactic and prosodic information and can be found in the unsupervised setting. Moreover, a novel supervised topic model is proposed to represent and learn the difference of languages. We built the system of language identification and reported the test results on the NIST LRE07 datasets and the Chinese dialect spoken corpus. Compared with other state-of-the-art methods, the experiment results show that the proposed method provides competitive performance and helps to capture robust discriminative information for short duration language identification.