Xiaoliang Wu

Xiaoliang Wu, Peter Bell, Ajitha Rajan

Explainable AI (XAI) techniques have been widely used to help explain and understand the output of deep learning models in fields such as image classification and Natural Language Processing. Interest in using XAI techniques to explain deep learning-based automatic speech recognition (ASR) is emerging. but there is not enough evidence on whether these explanations can be trusted. To address this, we adapt a state-of-the-art XAI technique from the image classification domain, Local Interpretable Model-Agnostic Explanations (LIME), to a model trained for a TIMIT-based phoneme recognition task. This simple task provides a controlled setting for evaluation while also providing expert annotated ground truth to assess the quality of explanations. We find a variant of LIME based on time partitioned audio segments, that we propose in this paper, produces the most reliable explanations, containing the ground truth 96% of the time in its top three audio segments.

Xiaoliang Wu, Ajitha Rajan

Automatic speech recognition (ASR) models are prevalent, particularly in applications for voice navigation and voice control of domestic appliances. The computational core of ASRs are deep neural networks (DNNs) that have been shown to be susceptible to adversarial perturbations; easily misused by attackers to generate malicious outputs. To help test the security and robustnesss of ASRS, we propose techniques that generate blackbox (agnostic to the DNN), untargeted adversarial attacks that are portable across ASRs. This is in contrast to existing work that focuses on whitebox targeted attacks that are time consuming and lack portability. Our techniques generate adversarial attacks that have no human audible difference by manipulating the audio signal using a psychoacoustic model that maintains the audio perturbations below the thresholds of human perception. We evaluate portability and effectiveness of our techniques using three popular ASRs and two input audio datasets using the metrics - Word Error Rate (WER) of output transcription, Similarity to original audio, attack Success Rate on different ASRs and Detection score by a defense system. We found our adversarial attacks were portable across ASRs, not easily detected by a state-of-the-art defense system, and had significant difference in output transcriptions while sounding similar to original audio.