This week's Deep Learning Paper Recaps cover two important topics in Automatic Speech Recognition (ASR). The first paper, "Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition," presents a novel method to generate audio samples that are both imperceptible and robust. These adversarial samples can be played over the air without losing effectiveness. While the imperceptible attack has a high success rate, there is room for improvement in combining it with a robust attack. The second paper, "Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition," proposes using adapters to reduce the number of parameters needed when fine-tuning pretrained wav2vec models for each downstream task. This approach allows reusing 90% of the parameters for each task, reducing deployment costs and improving efficiency.