CI-ADDO-NEW: Collaborative Research: The Speech Recognition Virtual Kitchen

对端到端语音处理问题进行成功的研究需要集成许多单独的工具（例如，用于数据清洗，声学模型训练，语言建模，数据分析，实时音频，解码，解析，合成等）。新研究人员很难在这个领域入门，原因很简单，因为典型的实验室环境是由适合特定计算设置的各种工具组成的大杂烩。这种环境很难重建，因为很少有人是所有这些领域的理论和实践专家，并且可以从头开始调试和复制实验。该研究基础设施项目创建了一个基于虚拟机（vm）的“厨房”环境，以促进研究技术的社区共享，并提供可靠的参考系统作为教育、研究和评估的工具。我们把虚拟机比作“厨房”，因为它们提供了一个环境，人们可以在其中安装“设备”（例如，工具包）、“食谱”（使用这些工具创建最先进系统的脚本）和“成分”（口语数据）。厨房里甚至有“参考菜肴”，包括基线运行、日志文件等完整的实验，以及重新创建和修改它们所需的一切。该项目通过(a)建立试点虚拟机，(b)让社区参与使用并继续自己开发虚拟机，以及(c)评估为教育和研究提供虚拟机的影响来开发社区和存储库。我们设想研究人员和学生下载一个VM，复制基线实验，实现更改，在社区中发布他们的结果，与其他在同一VM上工作的用户讨论，将改进合并回VM，重新分发，最终发布易于重复的结果。与课程和项目开发合作，将支持创造引人入胜的活动，特别鼓励本科生和研究生水平的学生。

The Speech Recognition Virtual Kitchen Performing successful research on end-to-end speech processing problems requires the integration of many individual tools (e.g. for data cleaning, acoustic model training, language modeling, data analysis, real-time audio, decoding, parsing, synthesis, etc.). It is difficult for new researchers to get started in the field, simply because a typical lab environment consists of a hodgepodge of tools suited to a particular computing set-ups. This environment is hard to recreate, because few people are experts in the theory and practice of all these fields, and can debug and replicate experiments from scratch. This research infrastructure project creates a "kitchen" environment based on Virtual Machines (VMs) to promote community sharing of research techniques, and provides solid reference systems as a tool for education, research, and evaluation. We liken VMs to a "kitchen" because they provide an environment into which one can install "appliances" (e.g., toolkits), "recipes" (scripts for creating state-of-the art systems using these tools), and "ingredients" (spoken language data). The kitchen even holds "reference dishes" in the form of complete experiments with baseline runs, log-files, etc., together with all that is needed to recreate and modify them. The project is developing a community and repository by (a) building pilot VMs, (b) engaging the community in using and continuing to develop them on its own, and (c) evaluating the impact of providing VMs for education and research. We envision researchers as well as students downloading a VM, reproducing the baseline experiment, implementing changes, posting their results in the community, discussing with other users who have worked on the same VM, merging improvements back into the VM, which get re-distributed, and finally publishing easily reproducible results. Work with curriculum and project development will support the creation of engaging activities to specifically encourage students at undergraduate and graduate levels.