Spoken language recognition (SLR) is the task of automatically identifying
the language present in a speech signal. Existing SLR models are either too
computationally expensive or too large to run effectively on devices with
limited resources. For real-world deployment, a model should also gracefully
handle unseen languages outside of the target language set, yet prior work has
focused on closed-set classification where all input languages are known
a-priori. In this paper we address these two limitations: we explore efficient
model architectures for SLR based on convolutional networks, and propose a
multilabel training strategy to handle non-target languages at inference time.
Using the VoxLingua107 dataset, we show that our models obtain competitive
results while being orders of magnitude smaller and faster than current
state-of-the-art methods, and that our multilabel strategy is more robust to
unseen non-target languages compared to multiclass classification.

本文主要讲述了如何利用卷积神经网络构建高效的口语语种识别模型，并在训练时采用多标签的方式来应对非目标语种的识别，实验结果表明，该模型相比当前最先进的方法在速度上有数量级的提升，并且在多标签分类任务中更加稳健。