We introduce TitaNet-LID, a compact end-to-end neural network for Spoken Language Identification (LID) that is based on the ContextNet architecture. TitaNet-LID employs 1D depth-wise separable convolutions and Squeeze-and-Excitation layers to effectively capture local and global context within an utterance. Despite its small size, TitaNet-LID achieves performance similar to state-of-the-art models on the VoxLingua107 dataset while being 10 times smaller. Furthermore, it can be easily adapted to new acoustic conditions and unseen languages through simple fine-tuning, achieving a state-of-the-art accuracy of 88.2% on the FLEURS benchmark. Our model is scalable and can achieve a better trade-off between accuracy and speed. TitaNet-LID performs well even on short utterances less than 5s in length, indicating its robustness to input length.

翻译：我们提出TitaNet-LID——一种基于ContextNet架构的紧凑型端到端神经网络，专用于口语语言识别（LID）。该模型采用一维深度可分离卷积与Squeeze-and-Excitation层，有效捕获语音片段内的局部与全局上下文信息。尽管模型体积小巧，TitaNet-LID在VoxLingua107数据集上仍能达到与当前最先进模型相当的识别性能，且参数量仅为后者的十分之一。此外，通过简单的微调操作，该模型可轻松适应新的声学环境与未见语言，在FLEURS基准测试中实现了88.2%的顶尖准确率。我们的模型具备可扩展性，能够在准确率与推理速度之间实现更优的权衡。即便处理长度不足5秒的短语音片段，TitaNet-LID仍表现出色，充分证明了其对输入时长的鲁棒性。