Deep Learning Paper Recap - Automatic Speech Recognition

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.

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