Language ID Prediction from Speech Using Self-Attentive Pooling

@article{Bedyakin2021LanguageIP,
  title={Language ID Prediction from Speech Using Self-Attentive Pooling},
  author={Roman Bedyakin and N. Mikhaylovskiy},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.11985}
}
This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. For many low-resource and endangered languages, only single-speaker recordings may be available, demanding a need for domain and speaker-invariant language ID systems. In this memo, we show that a convolutional neural network with a Self-Attentive Pooling layer… 
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