  Developers need to perform adequate testing to ensure the quality of
Automatic Speech Recognition (ASR) systems. However, manually collecting
required test cases is tedious and time-consuming. Our recent work proposes
CrossASR, a differential testing method for ASR systems. This method first
utilizes Text-to-Speech (TTS) to generate audios from texts automatically and
then feed these audios into different ASR systems for cross-referencing to
uncover failed test cases. It also leverages a failure estimator to find
failing test cases more efficiently. Such a method is inherently
self-improvable: the performance can increase by leveraging more advanced TTS
and ASR systems. So in this accompanying tool demo paper, we devote more
engineering and propose CrossASR++, an easy-to-use ASR testing tool that can be
conveniently extended to incorporate different TTS and ASR systems, and failure
estimators. We also make CrossASR++ chunk texts from a given corpus dynamically
and enable the estimator to work in a more effective and flexible way. We
demonstrate that the new features can help CrossASR++ discover more failed test
cases. Using the same TTS and ASR systems, CrossASR++ can uncover 26.2% more
failed test cases for 4 ASRs than the original tool. Moreover, by simply adding
one more ASR for cross-referencing, we can increase the number of failed test
cases uncovered for each of the 4 ASR systems by 25.07%, 39.63%, 20.9\% and
8.17% respectively. We also extend CrossASR++ with 5 additional failure
estimators. Compared to worst estimator, the best one can discover 10.41% more
failed test cases within the same amount of time.

Software Engineering