  Recent advances in Automatic Speech Recognition (ASR) demonstrated how
end-to-end systems are able to achieve state-of-the-art performance. There is a
trend towards deeper neural networks, however those ASR models are also more
complex and prone against specially crafted noisy data. Those Audio Adversarial
Examples (AAE) were previously demonstrated on ASR systems that use
Connectionist Temporal Classification (CTC), as well as attention-based
encoder-decoder architectures. Following the idea of the hybrid CTC/attention
ASR system, this work proposes algorithms to generate AAEs to combine both
approaches into a joint CTC-attention gradient method. Evaluation is performed
using a hybrid CTC/attention end-to-end ASR model on two reference sentences as
case study, as well as the TEDlium v2 speech recognition task. We then
demonstrate the application of this algorithm for adversarial training to
obtain a more robust ASR model.
