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一个程度，在原则上大多数挑战都可以得到满足，只要有一个基线系统，目标领域的足够数据，以及一位知道如何为目标使用环境开发或调整识别器的专家。不幸的是，这种方法不能扩展：尽管人们对语音用户界面越来越感兴趣，但有能力分析和开发准确语音识别器的专家数量有限。这项探索性研究的早期资助探索了在基于规则的知识库中形式化语音识别专家对所需分析和开发步骤的隐性知识的可能性，这可以帮助语音识别非专业人员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。语音识别专家通过倾听数据、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适应和改进识别器，这些都是基于他们对不匹配使用环境的评估。该项目从与这些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的适应技术中获得的收益，并探索使该方法可行的因素。该项目为更广泛的研究人员、学生和从业人员（特别是用户界面领域的人员）提供了开发支持语音的应用程序的途径。它将使联合开发用户界面和语音识别变得可行，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.