CAREER: A Graphical-Model Based Software Infrastructure for Speech Recognition Research and Education

这是一个为期5年的连续奖励的第一年资助。当前的核心语音识别技术在几个重要的情况下是不够的，在这些情况下，高错误率通常使该技术无法使用。 该项目将通过使用图形模型，一种相对较新的统计技术，包括用于推理随机过程的正式方法，以及以计算效率高的方式做出概率数据驱动决策的算法集，来推进最新技术。 在自动语音识别中使用的所有现有的统计技术（以及更多）似乎都可以使用图形模型表示;没有其他已知的抽象具有这样的属性。 PI将为语音识别研究和教育设计和构建新的免费开源软件。 该基础设施将提供成功研究基于不同图形模型的语音识别算法所需的工具，并将包括用于使用指定模型进行计算和搜索新模型的工具。 该基础设施将用于华盛顿大学（研究生和本科生）的课程开发，并将沿着课程材料在互联网上发布，供任何人免费使用。

This is the first year funding of a 5-year continuing award. Current core speech recognition technology is inadequate in several important situations, where high error rates often render the technology unusable. This project will advance the state of the art through use of graphical models, a relatively new statistical technique comprising formal methods for reasoning about random processes, and sets of algorithms for making probabilistic data-driven decisions in computationally efficient ways. All existing statistical techniques used in automatic speech recognition (and many more) appear to be representable using graphical models; no other known abstraction has such a property. The PI will design and build new freely available open-source software for speech recognition research and education. This infrastructure will provide the tools needed to successfully investigate different graphical-model based speech recognition algorithms, and will include tools for both computing with a specified model and for searching for new ones. The infrastructure will be used for curriculum development at the University of Washington (both at the graduate and undergraduate level), and will be released, along with course materials, on the Internet for free use by anyone.