In this paper, we explore the untapped potential of Whisper, a well-established automatic speech recognition (ASR) foundation model, in the context of L2 spoken language assessment (SLA). Unlike prior studies that extrinsically analyze transcriptions produced by Whisper, our approach goes a step further to probe its latent capabilities by extracting acoustic and linguistic features from hidden representations. With only a lightweight classifier being trained on top of Whisper's intermediate and final outputs, our method achieves strong performance on the GEPT picture-description dataset, outperforming existing cutting-edge baselines, including a multimodal approach. Furthermore, by incorporating image and text-prompt information as auxiliary relevance cues, we demonstrate additional performance gains. Finally, we conduct an in-depth analysis of Whisper's embeddings, which reveals that, even without task-specific fine-tuning, the model intrinsically encodes both ordinal proficiency patterns and semantic aspects of speech, highlighting its potential as a powerful foundation for SLA and other spoken language understanding tasks.

翻译：本文探讨了Whisper这一成熟的自动语音识别（ASR）基础模型在第二语言口语评估（SLA）领域中尚未被开发的潜力。与先前研究仅外部分析Whisper生成转录文本的方法不同，我们的研究进一步通过从隐藏表示中提取声学和语言特征，以探究其潜在能力。仅在Whisper的中间及最终输出层上训练一个轻量级分类器，我们的方法在GEPT图片描述数据集上取得了优异性能，超越了包括多模态方法在内的现有前沿基线模型。此外，通过引入图像和文本提示信息作为辅助相关性线索，我们展示了额外的性能提升。最后，我们对Whisper的嵌入表示进行了深入分析，结果表明即使未经任务特定微调，该模型本质上已编码了口语的序数能力模式与语义特征，凸显了其作为SLA及其他口语理解任务强大基础模型的潜力。