ASRTest: automated testing for deep-neural-network-driven speech recognition systems

Abstract

With the rapid development of deep neural networks and end-to-end learning techniques, automatic speech recognition (ASR) systems have been deployed into our daily and assist in various tasks. However, despite their tremendous progress, ASR systems could also suffer from software defects and exhibit incorrect behaviors. While the nature of DNN makes conventional software testing techniques inapplicable for ASR systems, lacking diverse tests and oracle information further hinders their testing. In this paper, we propose and implement a testing approach, namely ASR, specifically for the DNN-driven ASR systems. ASRTest is built upon the theory of metamorphic testing. We first design the metamorphic relation for ASR systems and then implement three families of transformation operators that can simulate practical application scenarios to generate speeches. Furthermore, we adopt Gini impurity to guide the generation process and improve the testing efficiency. To validate the effectiveness of ASRTest, we apply ASRTest to four ASR models with four widely-used datasets. The results show that ASRTest can detect erroneous behaviors under different realistic application conditions efficiently and improve 19.1% recognition performance on average via retraining with the generated data. Also, we conduct a case study on an industrial ASR system to investigate the performance of ASRTest under the real usage scenario. The study shows that ASRTest can detect errors and improve the performance of DNN-driven ASR systems effectively.