Native Language Identification Using Spectral and Source-Based Features

The task of native language (L1) identification from nonnative language (L2) can be thought of as the task of identifying the common traits that each group of L1 speakers maintains while speaking L2 irrespective of the dialect or region. Under the assumption that speakers are L1 proficient, non-native cues in terms of segmental and prosodic aspects are investigated in our work. In this paper, we propose the use of longer duration cepstral features, namely, Mel frequency cepstral coefficients (MFCC) and auditory filterbank features learnt from the database using Convolutional Restricted Boltzmann Machine (ConvRBM) along with their delta and shifted delta features. MFCC and ConvRBM gave accuracy of 38.2% and 36.8%, respectively, on the development set provided for the ComParE 2016 Nativeness Task using Gaussian Mixture Model (GMM) classifier. To add complementary information about the prosodic and excitation source features, phrase information and its dynamics extracted from the log(F0) contour of the speech was explored. The accuracy obtained using score-level fusion between system features (MFCC and ConvRBM) and phrase features were 39.6% and 38.3%, respectively, indicating that phrase information and MFCC capture complementary information than ConvRBM alone. Furthermore, score-level fusion of MFCC, ConvRBM and phrase improves the accuracy to 40.2%.