论文标题
I2CR：使用Intra Intra对比度正规化，改善关键字斑点上的噪声鲁棒性
I2CR: Improving Noise Robustness on Keyword Spotting Using Inter-Intra Contrastive Regularization
论文作者
论文摘要
关键字斑点中的噪声稳健性仍然是一个挑战，因为许多模型无法克服噪音的重大影响，从而导致特征嵌入质量的恶化。我们提出了一种称为Intra Intra Intra对比度正则化（I2CR）的对比正则化方法，以通过指导模型学习特定于集群的基本语音信息来改善特征表示。这涉及最大化同一类内部和样本之间的相似性。结果，它使实例更接近更广泛的表示形成更突出的群集，并减少了噪音的不利影响。我们表明，我们的方法在不同的噪声环境下的不同骨干模型体系结构方面提供了准确性的一致性提高。我们还证明，我们提出的框架提高了看不见的室外噪声和看不见的变体噪声SNR的准确性。这表明我们工作的重要性是噪声稳健性的整体完善。
Noise robustness in keyword spotting remains a challenge as many models fail to overcome the heavy influence of noises, causing the deterioration of the quality of feature embeddings. We proposed a contrastive regularization method called Inter-Intra Contrastive Regularization (I2CR) to improve the feature representations by guiding the model to learn the fundamental speech information specific to the cluster. This involves maximizing the similarity across Intra and Inter samples of the same class. As a result, it pulls the instances closer to more generalized representations that form more prominent clusters and reduces the adverse impact of noises. We show that our method provides consistent improvements in accuracy over different backbone model architectures under different noise environments. We also demonstrate that our proposed framework has improved the accuracy of unseen out-of-domain noises and unseen variant noise SNRs. This indicates the significance of our work with the overall refinement in noise robustness.
