EAGER: A Research Infrastructure for Analyzing Speech-based Interfaces

EAGER：用于分析基于语音的界面的研究基础设施

• 批准号: 1247368
• 负责人: Florian Metze
• 金额: $ 10万
• 依托单位: Carnegie-Mellon University
• 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
• 起止时间: 2012-08-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247368&HistoricalAwards=false
• 关键词: EAGER; Research; Infrastructure; Analyzing; Speech

The research community's understanding of the speech-to-text problem has reached a point at which most challenges can in principle be met, given a baseline system, enough data from the target domain, and an expert, who knows how to develop or adapt a recognizer for the target context-of-use. Unfortunately, this approach does not scale: despite the growing interest in speech-user interfaces, there are a limited number of experts equipped to analyze and develop an accurate speech recognizer.This Early Grant for Exploratory Research explores the possibility of formalizing a speech recognition expert's implicit knowledge of the required analysis and development steps in a rule-based knowledge base, which can help a speech recognition non-expert develop a speech recognizer as part of an application, such as a dialog system in a rare dialect. Speech recognition experts adapt and improve recognizers by listening to data, aggregating error reports, and then adjusting parameters, retraining models, or applying adaptation techniques, based on their assessment of the mismatched context of use. This project extracts intuition from contextual interviews with such experts, develops a proof-of-concept expert system to predict the gains a system would see from specific adaptation techniques, and explores the factors which will make this approach feasible.This project creates ways to make development of speech-enabled applications more accessible to a broader class of researchers, students, and practitioners, particularly from the user interface area. It will make joint development of user interface and speech recognition feasible, without requiring large teams with varied skill-sets.

研究界对语音到文本问题的理解已经达到了原则上可以应对大多数挑战的程度，只要有基线系统、来自目标领域的足够数据以及知道如何开发或调整语音到文本的专家。目标使用上下文的识别器。不幸的是，这种方法无法扩展：尽管人们对语音用户界面的兴趣日益浓厚，但能够分析和开发准确语音识别器的专家数量有限。这项早期探索性研究资助探索了将语音识别专家的能力形式化的可能性。基于规则的知识库中所需分析和开发步骤的隐式知识，这可以帮助语音识别非专家开发语音识别器作为应用程序的一部分，例如罕见方言的对话系统。语音识别专家根据对不匹配的使用环境的评估，通过听取数据、汇总错误报告，然后调整参数、重新训练模型或应用适应技术来调整和改进识别器。该项目从与此类专家的情境访谈中提取直觉，开发概念验证专家系统来预测系统从特定适应技术中获得的收益，并探索使这种方法可行的因素。该项目创造了方法开发支持语音的应用程序，让更多的研究人员、学生和从业者更容易使用，尤其是用户界面领域的应用程序。它将使得用户界面和语音识别的联合开发变得可行，而不需要具有不同技能的大型团队。