  This work focuses on improving the Spoken Language Identification (LangId)
system for a challenge that focuses on developing robust language
identification systems that are reliable for non-standard, accented
(Singaporean accent), spontaneous code-switched, and child-directed speech
collected via Zoom. We propose a two-stage Encoder-Decoder-based E2E model. The
encoder module consists of 1D depth-wise separable convolutions with
Squeeze-and-Excitation (SE) layers with a global context. The decoder module
uses an attentive temporal pooling mechanism to get fixed length
time-independent feature representation. The total number of parameters in the
model is around 22.1 M, which is relatively light compared to using some
large-scale pre-trained speech models. We achieved an EER of 15.6% in the
closed track and 11.1% in the open track (baseline system 22.1%). We also
curated additional LangId data from YouTube videos (having Singaporean
speakers), which will be released for public use.
