OIA/MRI: Acquisition of a Computational Server for Large Vocabulary Connectionist Speech Recognition

OIA/MRI：购买用于大词汇量联结语音识别的计算服务器

• 批准号: 0521210
• 负责人: Nelson Morgan
• 金额: $ 28.08万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0521210&HistoricalAwards=false
• 关键词: OIA; MRI; Acquisition; Computational; Server

This project, supporting experimental research methods for large speech recognition tasks, aims at purchasing a large Symmetric Multi-Processor (SMP) system. The research involves the development of models and algorithms that will reduce automatic speech recognition (ASR) errors for natural conversations, which may be exacerbated by realistic but difficult acoustic conditions. Major improvement in algorithm robustness opens a wider range of future applications, including voice access to networked information and information retrieval and extraction for meetings. Head-mounted microphones or microphone arrays may not be feasible due to low Signal to Noise Ratio (SNR) and the effects of reverberation. Integration of multiple estimators, either at the level of probability streams or hypothesized word sequences, with associated confidence measures, can greatly improve overall performance. Research has shown that such properties can significantly increase recognition accuracy, even for high SNR tasks that require the transcription of informal conversational speech. For problems of scale, training of even a single-stream system can take weeks using a 2005-generation PC or workstation. A fast multi-processor system might overcome these resource limitations and greatly enhance the ability to explore promising solutions to the current constraints on performance. Hence, research requiring multiple probability streams or more computationally intensive algorithms should benefit from this new multi-layered system infrastructure.Broader Impact: The planned research supports technical explorations that become the basis of PhD dissertations. Other areas, such as computational biology, natural language processing, digital communications, computer vision, human activity modeling, and human computer interaction, might also benefit by the research. ICSI involves many female researchers; has a high school outreach program, and trains students, and other investigators.

本计画为支援大型语音辨识任务之实验研究方法，目的在购买一个大型对称多处理器系统。该研究涉及模型和算法的开发，这些模型和算法将减少自然对话的自动语音识别（ASR）错误，这些错误可能会因现实但困难的声学条件而加剧。算法鲁棒性的重大改进开辟了更广泛的未来应用，包括语音访问网络信息和会议信息检索和提取。由于低信噪比（SNR）和混响的影响，头戴式麦克风或麦克风阵列可能不可行。多个估计的集成，无论是在概率流或假设的单词序列的水平，与相关的置信度措施，可以大大提高整体性能。研究表明，这些属性可以显着提高识别精度，即使是对于需要转录非正式会话语音的高SNR任务。对于规模问题，使用2005代PC或工作站，即使是单流系统的培训也可能需要数周时间。一个快速的多处理器系统可能会克服这些资源限制，并大大提高能力，探索有前途的解决方案，目前的性能限制。因此，需要多个概率流或计算密集型算法的研究应该受益于这种新的多层系统基础设施。更广泛的影响：计划中的研究支持成为博士论文基础的技术探索。其他领域，如计算生物学，自然语言处理，数字通信，计算机视觉，人类活动建模和人机交互，也可能受益于这项研究。卵胞浆内单精子注射涉及许多女性研究人员;有一个高中外展计划，并培训学生和其他研究人员。