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CI-P:Collaborative Research:The Speech Recognition Virtual Kitchen

CI-P:Collaborative Research:The Speech Recognition Virtual Kitchen
CI-P:协作研究:语音识别虚拟厨房
批准号:
1205424
负责人:
Eric Fosler-Lussier
金额:
$4.85万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-06-01 至 2013-05-31

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中文摘要
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英文摘要
CI-P:Collaborative Research: The Speech Recognition Virtual Kitchen This project provides a "kitchen" environment to promote community sharing of research techniques, foster innovative experimentation, and provide solid reference systems for automatically recognizing speech, as a tool for education, research, and evaluation. The research infrastructure is built around virtual machines (VMs), which can be reconfigured and shared easily. We liken the virtual machines to a "kitchen" because they provide the environmental infrastructure into which one can install "appliances" (e.g., speech recognition toolkits), "recipes" (scripts for creating state-of-the art systems for a toolkit), and "ingredients" (spoken language data), along with "dishes" (completed experiments with log-files for reference). The planning project engages the community to tackle some of the issues that have previously hampered efforts in cross-community sharing, including distribution methods and intellectual property issues. The project also provides an example architecture, which serves as a focus point for community-wide discussion. In terms of broader impacts, the project engages researchers and educators that typically do not participate in automatic speech recognition (ASR) research by providing travel scholarships to a workshop at INTERSPEECH2012. In a wider scope, the infrastructure may be useable by other data-intensive fields (synthesis, dialog systems, NLP, computer vision, data mining). By providing a permanent, publicly available resource for research, education, and evaluation in ASR research, we can better train the next generation of undergraduates and graduates. The "kitchen" gives them easy access to a large number of state-of-the-art implementations, and facilitates deeper analysis of algorithms and better comparisons across systems.
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