Abstract

A critical challenge to automatic language identification (LID) is achieving accurate performance with the shortest possible speech segment in a rapid fashion. The accuracy to correctly identify the spoken language is highly sensitive to the duration of speech and is bounded by the amount of information available. The proposed approach for rapid language identification transforms the utterances to a low dimensional i-vector representation upon which language classification methods are applied. In order to meet the challenges involved in rapidly making reliable decisions about the spoken language, a highly accurate and computationally efficient framework of i-vector extraction is proposed. The LID framework integrates the approach of universal background model (UBM) fused total variability modeling. UBM-fused modeling yields the estimation of a more discriminant, single i-vector space. This way, it is also a …

Date

April 6, 2015

Authors

Maarten Van Segbroeck, Ruchir Travadi, Shrikanth S Narayanan

Journal

IEEE/ACM Transactions on Audio, Speech, and Language Processing

Volume

23

Issue

7

Pages

1118-1129

Publisher

IEEE