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点，在这个点上，大多数挑战在原则上都可以得到满足，只要有一个基线系统，足够的数据来自目标域，以及一个专家，他知道如何开发或调整识别器的目标上下文的使用。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越来越大，但具备分析和开发精确语音识别器能力的专家数量有限。这项探索性研究早期资助旨在探索将语音识别专家对所需分析和开发步骤的隐含知识形式化的可能性，其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。语音识别专家通过倾听数据、汇总错误报告，然后根据对不匹配的使用环境的评估调整参数、重新训练模型或应用自适应技术来调整和改进识别器。这个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从特定的适应技术中获得的收益，并探索使这种方法可行的因素。这个项目创造了使语音应用程序的开发更容易被更广泛的研究人员，学生和从业人员，特别是从用户界面领域。它将使用户界面和语音识别的联合开发变得可行，而不需要具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.