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

• URL: http://arxiv.org/abs/2104.11985v1
• Date: Sat, 24 Apr 2021 16:41:17 GMT
• Title: Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions
• Authors: Roman Bedyakin, Nikolay Mikhaylovskiy
• Abstract summary: This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech. 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. We show that a convolutional neural network with a Self-Attentive Pooling layer shows promising results for the language identification task.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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