MRI: Acquisition of Tesla Hardware for Speech Recognition

提案#：CNS09-23511 Pi(S)：Janin，Adam Institute：International Computer Science Institute标题：mri/Acq.：收购用于语音识别的Tesla硬件项目建议：该项目收购了NVIDIA Tesla架构系统，促进了用于自动语音识别的并行代码算法的开发。用于语音识别的特斯拉硬件由10个NVIDIA特斯拉机架安装单元和相关基础设施组成。由于计算机系统现在遵守S定律，必须开发新的并行代码来提高语音识别的准确性。(芯片上的核心数量每两年翻一番)。该系统提供了一个大规模多核通用计算环境，使可扩展并行代码算法的开发成为可能。该仪器提供了一个试验台，用于研究未来的可扩展算法，预计这些算法将在先进和新颖的算法方面继续在商业和工业市场上保持领先地位。因此，展望5到10年后多核架构盛行时的计算状态，该项目的目标是首先确保用于语音识别研究的适当计算资源，然后剥离当前的2核和4核基本桌面系统。该研究所在具有挑战性的声学特性和自然的人与人之间的交流的现实环境中为机器学习和语音识别提供广泛的培训。应用程序从对残疾用户的免提访问和为不懂计算机的人提供的自然语音驱动界面到自动会议助手和浏览器，其中会议被实时记录，并且提供了允许在会议期间和之后访问内容的工具。以下两种相关方法提高了准确率：-涉及系统内多个级别的组合的多数据流方法，包括多个特征、多个机器学习估计器和多个词流组合以及-增加训练集的大小。即使在训练数据与实际应用条件不完全匹配的情况下，小心地整合数据也可以提高准确性。这两种方法都需要增加计算能力，这是目前传统硬件难以满足的。更广泛的影响：收购有助于继续吸引年轻研究人员并帮助他们进行培训。计算能力的提高有利于向当地高中生演示语音研究。BFOIT信息技术机会基金会旨在吸引更多的女性和在计算机科学和工程领域代表性不足的少数族裔。

Proposal #: CNS 09-23511 PI(s): Janin, Adam Institution: International Computer Science InstituteTitle: MRI/Acq.: Acquisition of Tesla Hardware for Speech Recognition Project Proposed:This project, acquiring an nVidia Tesla architecture system, facilitates development parallel code algorithms for automatic speech recognition (ASR). The Tesla Hardware for Speech Recognition consists of a cluster of 10 nVidia Tesla rack-mounted units with associated infrastructure. New parallel codes must be developed to improve the accuracy of speech recognition, since computer systems now obey ?Core?s Law? (where the number of cores on a chip doubles once every two years). The system provides a large-scale multi-core general purpose computing environment that enables the development of scalable parallel code algorithms. The instrument provides a testbed in which to investigate future scalable algorithms that are expected to facilitate continued leadership in the commercial and industrial markets in terms of advanced and novel algorithms. Thus, envisioning the state of computing in 5 to 10 years when multi-core architectures prevail, the project aims to first- Secure appropriate computing resources for research in speech recognition and then- Eclipse the current 2- and 4-core basic desktop systems.The institute performs extensive training for machine learning and speech recognition in realistic settings with challenging acoustic properties and natural, human to human communication. Applications run from hands-free access to disabled users and natural speech-driven interfaces for the non-computer literate to automatic meeting assistants and browsers, in which meetings are recorded in real-time and tools are provided that allow access to content both during and after the meeting. The following two relevant methods improve accuracy:- Multi-stream methods that involve combinations at many levels within the system, including multiple features, multiple machine learning estimators, and multiple word-streams combinations and- Increasing the size of the training set.Careful integration of data can improve the accuracy even when the training data does not exactly match the conditions of actual application. Both methods require increasing computational power hard to fulfill with current conventional hardware.Broader Impacts: The acquisition contributes to continue attracting young researchers and aiding in their training. The improved computational capability facilitates the demonstration of speech research to local high school students. The BFOIT Foundation for Opportunities in Information Technology aims to attract more women and underrepresented minorities in computer science and engineering.