Matlab/Simulink is a development and simulation language that is widely used by the Cyber-Physical System (CPS) industry to model dynamical systems. There are two mainstream approaches to verify CPS Simulink models: model testing that attempts to identify failures in models by executing them for a number of sampled test inputs, and model checking that attempts to exhaustively check the correctness of models against some given formal properties. In this paper, we present an industrial Simulink model benchmark, provide a categorization of different model types in the benchmark, describe the recurring logical patterns in the model requirements, and discuss the results of applying model checking and model testing approaches to identify requirements violations in the benchmarked models. Based on the results, we discuss the strengths and weaknesses of model testing and model checking. Our results further suggest that model checking and model testing are complementary and by combining them, we can significantly enhance the capabilities of each of these approaches individually. We conclude by providing guidelines as to how the two approaches can be best applied together.

We have collected a corpus of 78 hours of speech from 297 elderly speakers, with an average age of 79. We find that acoustic models built from elderly speech provide much better recognition than do non-elderly models (42.1 vs. 54.6% WER). We also find that elderly men have substantially higher word error rates than elderly women (typically 14% absolute). We report on other experiments with this corpus, dividing the speakers by age, by gender, and by regional accent. Using the resulting "elderly acoustic model", we built a document-retrieval program that can be operated by voice or typing. After usability tests with 110 speakers, we tested the final system on 37 elderly speakers. Each retrieved 4 documents from a database of 86,190 Boston Globe articles, 2 by typing and 2 by speech. We measured how quickly they retrieved each article, and how much help they required. We find no difference between spoken and typed queries in either retrieval times or in amount of help required, regardless of age, gender, or computer experience. However, users perceive speech to be substantially faster, and overwhelmingly prefer speech to typing.

In our experiment, 30 computer-literate elderly speakers (15 male, 15 female) with no previous ASR experience were given 2 hours of intensive training in using a speech recognition system. Before and after this training session, they were asked to read separate 520-word texts. Measuring the word error rates (WERs) on these “before training” and “after training” recordings, we find a small but statistically significant improvement. Before training, speakers had an average WER of 20.9%, and after training, 19.8%. We examine changes in speaking rate, phrase length, and SNR and their impact on WER.