  In this paper, we propose to employ a dual-mode framework on the x-vector
self-attention (XSA-LID) model with knowledge distillation (KD) to enhance its
language identification (LID) performance for both long and short utterances.
The dual-mode XSA-LID model is trained by jointly optimizing both the full and
short modes with their respective inputs being the full-length speech and its
short clip extracted by a specific Boolean mask, and KD is applied to further
boost the performance on short utterances. In addition, we investigate the
impact of clip-wise linguistic variability and lexical integrity for LID by
analyzing the variation of LID performance in terms of the lengths and
positions of the mimicked speech clips. We evaluated our approach on the MLS14
data from the NIST 2017 LRE. With the 3~s random-location Boolean mask, our
proposed method achieved 19.23%, 21.52% and 8.37% relative improvement in
average cost compared with the XSA-LID model on 3s, 10s, and 30s speech,
respectively.
