We present a novel recurrent neural network (RNN) model for voice activity detection. Our multi-layer RNN model, in which nodes compute quadratic polynomials, outperforms a much larger baseline system composed of Gaussian mixture models (GMMs) and a hand-tuned state machine (SM) for temporal smoothing. All parameters of our RNN model are optimized together, so that it properly weights its preference for temporal continuity against the acoustic features in each frame. Our RNN uses one tenth the parameters and outperforms the GMM+SM baseline system by 26% reduction in false alarms, reducing overall speech recognition computation time by 17% while reducing word error rate by 1% relative.

As modern operating systems become more complex, understanding their inner workings is increasingly difficult. Dynamic kernel instrumentation is a well established method of obtaining insight into the workings of an OS, with applications including debugging, profiling and monitoring, and security auditing. To date, all dynamic instrumentation systems for operating systems follow the probe-based instrumentation paradigm. While efficient on fixed-length instruction set architectures, probes are extremely expensive on variable-length ISAs such as the popular Intel x86 and AMD x86-64. We propose using just-in-time (JIT) instrumentation to overcome this problem. While common in user space, JIT instrumentation has not until now been attempted in kernel space. In this work, we show the feasibility and desirability of kernel-based JIT instrumentation for operating systems with our novel prototype, implemented as a Linux kernel module. The prototype is fully SMP capable. We evaluate our prototype against the popular Kprobes Linux instrumentation tool. Our prototype outperforms Kprobes, at both micro and macro levels, by orders of magnitude when applying medium- and fine-grained instrumentation.