I‐vector representation based on bottleneck features for language identification

Abstract

An i-vector representation based on bottleneck (BN) features is presented for language identification (LID). In the proposed system, the BN features are extracted from a deep neural network, which can effectively mine the contextual information embedded in speech frames. The i-vector representation of each utterance is then obtained by applying a total variability approach on the BN features. The resulting performance of LID has been significantly improved with the proposed BN feature based i-vector representation. Compared with the state-of-the-art techniques, the equal error rate is relatively reduced by about 40% on the National Institute of Standards and Technology (NIST) 2009 evaluation sets.