Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 24 Apr 2021.
Builds on
7 of 28 references.
References · 7
- 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
- 2019QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutionscited by 93
- 2020Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trendscited by 28
- 2017Attention-Based Models for Text-Dependent Speaker Verificationcited by 17
- 2017Language Identification Using Deep Convolutional Recurrent Neural Networkscited by 7
- 2019Spoken Language Identification using ConvNetscited by 6
- 2020Triplet Entropy Loss: Improving The Generalisation of Short Speech Language Identification Systemscited by 3
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Citing papers per year: 2021: 1.
1 paper.