Effective extraction and application of linguistic features are central to
the enhancement of spoken Language IDentification (LID) performance. With the
success of recent large models, such as GPT and Whisper, the potential to
leverage such pre-trained models for extracting linguistic features for LID
tasks has become a promising area of research. In this paper, we explore the
utilization of the decoder-based network from the Whisper model to extract
linguistic features through its generative mechanism for improving the
classification accuracy in LID tasks. We devised two strategies - one based on
the language embedding method and the other focusing on direct optimization of
LID outputs while simultaneously enhancing the speech recognition tasks. We
conducted experiments on the large-scale multilingual datasets MLS,
VoxLingua107, and CommonVoice to test our approach. The experimental results
demonstrated the effectiveness of the proposed method on both in-domain and
out-of-domain datasets for LID tasks.

探索利用 Whisper 模型的解码器网络通过其生成机制提取语言特征来提高 LID 任务中的分类准确性。通过基于语言嵌入方法和直接优化 LID 输出的两种策略，在 MLS、VoxLingua107 和 CommonVoice 等大规模多语言数据集上进行实验以验证我们的方法的有效性。实验结果表明该方法在 LID 任务的领域内和领域外数据集上均具有良好效果。