Task-aware Deep Bottleneck Features for Spoken Language Identification

Recently, deep bottleneck features (DBF) extracted from a deep neural network (DNN) containing a narrow bottleneck lay-er, have been applied for language identification (LID), and yield significant performance improvement over state-of-the-art methods on NIST LRE 2009. However, the DNN is trained us-ing a large corpus of specific language which is not directly related to the LID task. More recently, lattice based discrimi-native training methods for extracting more targeted DBF were proposed for ASR. Inspired by this, this paper proposes to tune the post-trained DNN parameters using an LID-specific train-ing corpus, which may make the resulting DBF, termed a Dis-criminative DBF (D2BF), more discriminative and task-aware. Specifically, the maximum mutual information (MMI) criteri-on, with gradient descent, is applied to update the DNN param-eters of the bottleneck layer in an iterative fashion. We evaluate the performance of the proposed D2BF using different back-end models, including GMM-MMI and ivector, over the most con-fused 6-languages selected from NIST LRE 2009. The results show that the proposed D2BF is more appropriate and effective than the original DBF. Index Terms: language identification, deep bottleneck feature, deep neural network, discriminative training, Gaussian mixture model, maximum mutual information 1