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arXiv · 2104.11985

Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions

Roman Bedyakin·Nikolay Mikhaylovskiy
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Abstract

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 shows promising results for the language identification task.

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Roman Bedyakin, Nikolay Mikhaylovskiy. 2021-04-24. Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions. https://doi.org/10.18653/v1/2021.sigtyp-1.12

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