This paper introduces a two-pass on-device automatic speech recognition (ASR) system, which is developed for commercialized devices. The first pass of the system is based on a causal Conformer-transducer model to generate partial results from the input audio stream. After processing an entire input utterance in the first pass, the candidates for the final result are rescored with a full-context attention model in the second pass. To minimize the computational overhead from rescoring, we compress the full-context model by applying knowledge distillation (KD). The total model size is reduced by 35% after KD with a 0.02% absolute loss in word error rate (WER). We also introduce decoding techniques to boost the accuracy on the test cases mismatched with the distribution of the training set. The techniques include on-device personal adaptation, spell correction and handling incorrectly segmented speech, which solve the critical issues for production-grade systems. The whole system including the two-pass end-to-end (E2E) model and a language model (LM) occupies 72MB in storage after 8-bit quantization. We demonstrate the entire system on mobile devices and report results on test sets collected from the production environment. The developed system achieves 5.65% WER which surpasses the baseline system with 39% relative WER improvement.

The demand for a tool for summarizing emerging topics is increasing in modern life since the tool can deliver well-organized information to its users. Even though there are already a number of successful search systems, the system which automatically summarizes and organizes the content of emerging topics is still in its infancy. To fulfill such demand, we introduce an automated report generation system that generates a well-summarized human-readable report for emerging topics. In this report generation system, emerging topics are automatically discovered by a topic model and news articles are indexed by the discovered topics. Then, a topical summary and a timeline summary for each topic is generated by a topical multi-document summarizer and a timeline summarizer respectively. In order to enhance the apprehensibility of the users, the proposed report system provides two report modes. One is Today's Briefing which summarizes five discovered topics of every day, and the other is Full Report which shows a long-term view of each topic with a detailed topical summary and an important event timeline.

In this paper, we present a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus. We attained around 90% of a word recognition rate for general domain mainly by using joint training of connectionist temporal classifier (CTC) and cross entropy (CE) losses, minimum word error rate (MWER) training, layer-wise pretraining and data augmentation methods. In addition, we compressed our models by more than 3.4 times smaller using an iterative hyper low-rank approximation (LRA) method while minimizing the degradation in recognition accuracy. The memory footprint was further reduced with 8-bit quantization to bring down the final model size to lower than 39 MB. For on-demand adaptation, we fused the MoChA models with statistical n-gram models, and we could achieve a relatively 36% improvement on average in word error rate (WER) for target domains including the general domain.

Large OS kernels always suffer from attacks due to their numerous inherent vulnerabilities. To protect the kernel, hypervisors have been employed by many security solutions. However, relying on a hypervisor has a detrimental impact on the system performance due mainly to nested paging. In this paper, we present Hypernel, a security framework combining hardware and software components to address this problem. Hypersec, the software component, provides an isolated execution environment for security solutions, and the hardware monitor component enables a word-granularity monitoring capability on the kernel memory. Our evaluation shows that Hypernel efficiently fulfills the role of a security framework, while imposing mere 3.1% of runtime overhead on the system.