i‐vector representation based on bottleneck features for language identification
Yan Song, Bing Jiang, Yebo Bao, Si Wei, Lirong Dai · Electronics Letters · 2013
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.