AmberNet: A Compact End-to-End Model for Spoken Language Identification

10/27/2022

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by   Fei Jia, et al.

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We present AmberNet, a compact end-to-end neural network for Spoken Language Identification. AmberNet consists of 1D depth-wise separable convolutions and Squeeze-and-Excitation layers with global context, followed by statistics pooling and linear layers. AmberNet achieves performance similar to state-of-the-art(SOTA) models on VoxLingua107 dataset, while being 10x smaller. AmberNet can be adapted to unseen languages and new acoustic conditions with simple finetuning. It attains SOTA accuracy of 75.8 show the model is easily scalable to achieve a better trade-off between accuracy and speed. We further inspect the model's sensitivity to input length and show that AmberNet performs well even on short utterances.