EAGER: A Research Infrastructure for Analyzing Speech-based Interfaces

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，前提是有一个基线系统，来自目标领域的足够数据，以及一位知道如何为目标使用背景开发或调整识别器的专家。不幸的是，这种方法没有规模：尽管人们对语音用户界面的兴趣与日俱增，但具备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赠款探索了将语音识别专家关于所需分析和开发步骤的隐含知识在基于规则的知识库中正式化的可能性，这可以帮助语音识别非专家将语音识别器开发为应用程序的一部分，例如罕见方言的对话系统。语音识别专家通过收听数据、汇总错误报告、然后基于他们对不匹配的使用环境的评估来调整参数、再训练模型或应用适应技术来调整和改进识别器。该项目从与这类专家的上下文访谈中提取直觉，开发一个概念验证专家系统来预测系统将从特定适配技术中获得的收益，并探索使该方法可行的因素。该项目创造了使更多类别的研究人员、学生和从业者更容易开发语音应用程序的方法，特别是从用户界面区域。它将使联合开发用户界面和语音识别成为可能，而不需要拥有各种技能的大型团队。

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.