Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions. (arXiv:2106.00052v1 [eess.AS]) Leave a comment

This memo describes NTR/TSU winning submission for Low Resource ASR challenge
at Dialog2021 conference, language identification track.

Spoken Language Identification (LID) is an important step in a multilingual
Automated Speech Recognition (ASR) system pipeline. Traditionally, the ASR task
requires large volumes of labeled data that are unattainable for most of the
world’s languages, including most of the languages of Russia. In this memo, we
show that a convolutional neural network with a Self-Attentive Pooling layer
shows promising results in low-resource setting for the language identification
task and set up a SOTA for the Low Resource ASR challenge dataset.

Additionally, we compare the structure of confusion matrices for this and
significantly more diverse VoxForge dataset and state and substantiate the
hypothesis that whenever the dataset is diverse enough so that the other
classification factors, like gender, age etc. are well-averaged, the confusion
matrix for LID system bears the language similarity measure.

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