Profiling Large-Vocabulary Continuous Speech Recognition On Embedded Devices: A Hardware Resource Sensitivity Analysis

When deployed in embedded systems, speech recognizers are necessarily reduced from large-vocabulary continuous speech recognizers (LVCSR) found on desktops or servers to fit the limited hardware. However, embedded hardware continues to evolve in capability; today's smartphones are vastly more powerful than their recent ancestors. This begets a new question: which hardware features not currently found on today's embedded platforms, but potentially add-ons to tomorrow's devices, are most likely to improve recognition performance? Said differently what is the sensitivity of the recognizer to fine-grain details of the embedded hardware resources? To answer this question rigorously and quantitatively, we offer results from a detailed study of LVCSR performance as a function of microarchitecture options on an embedded ARM 11 and an enterprise-class Intel Core2Duo. We estimate speed and energy consumption, and show, feature by feature, how hardware resources impact recognizer performance.