2003 Symposium on Next Generation Automatic Speech Recognition (ASR)

尽管在过去的二十年里，使用模式匹配范例的自动语音识别（ASR）取得了显著的进步，但最近进展开始放缓。此外，目前最先进的技术还没有与人类的语音识别能力相媲美。一个潜在的改进领域是找到方法来评估和整合ASR社区之外开发的大量知识源，并建立开放共享的基础设施，以促进ASR的合作研究。由于语音识别技术对于实现人人都能获得信息的承诺至关重要，因此现在正是研究社区可以采取的弥合性能差距的选择的时候。该项目将组织一个为期两天的高度集中的研讨会，邀请美国学术界、工业界和政府的顶尖人士评估最先进的ASR技术的能力和局限性，并讨论加速ASR进展的潜在方法。研讨会的主题将集中在整合多学科的知识来源，从声学，语音，语言学，人机交互，认知科学，信号处理和计算机科学，到ASR组件和系统设计的每个阶段。会议结束时，所有介绍材料将整理成一份报告，分发给研究界和其他利益攸关方。这项研究应该激发ASR的新方向，为语音科学和技术的研究和教育概念化新的网络基础设施，并建议降低ASR进入门槛的方法，并通过建立多样化的研究人员社区来增加人类语言和通信研究的人才库。

Although there have been significant advances in automatic speech recognition (ASR) over the last twenty years using a pattern-matching paradigm, the progress has begun to slow recently. Furthermore, the current state-of-the-art has yet to rival human speech recognition capability. One potential area for improvement is to find ways to evaluate and incorporate the vast knowledge sources developed outside the ASR community and establish open sharable infrastructures to facilitate collaborative ASR research. As speech recognition technology is critical to realize the promise of universal access to information for all people, it is timely to examine options the community can take up to bridge the performance gap.This project is to organize a highly focused two-day symposium and invite top minds in US academia, industry and government to assess the capabilities and limitations of state-of-the-art ASR technologies, and discuss potential approaches to accelerating ASR advances. The symposium theme will be centered on integrating multi-disciplinary sources of knowledge, from acoustics, speech, linguistics, human computer interaction, cognitive science, signal processing, and computer science, into every stage of ASR component and system design. At the conclusion of the meeting, all the presentation materials will be collated into a report and disseminated to the research community and other stakeholders. This research should inspire new directions in ASR, conceptualize new cyber-infrastructures for research and education in speech science and technology, and recommend ways to lower ASR entry barriers and increase the talent pool in human language and communication research by enabling a diverse community of researchers.