Spoken Language Identification with Pre-trained Models and Margin Loss
For the speaker-controlled spoken language identification task proposed in the TidyLang Challenge 2026, this paper proposes a language identification method based on pre-trained models and margin-based losses. The proposed method adopts a pre-trained ECAPA-TDNN as the feature encoder and incorporates margin-based losses to enhance the discriminative ability of language representations, thereby improving inter-class separability and reducing the interference of non-linguistic factors such as speaker characteristics. Experimental results on the Tidy-X dataset show that the proposed method achieves 85.95% macro accuracy and 90.96% micro accuracy on the language identification task and 17.08%...
| Authors | Zhihua Fang, Liang He, Weiwu Jiang |
|---|---|
| Primary category | cs.SD |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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