Efficient configuration of robust spoken language technology based human interfaces

自动语音识别（ASR）算法和实现的发展导致了围绕该技术构建的大量应用程序和服务。ASR技术出现在为人机交互、语音听写、基于语音的音频和视频资源索引以及提高各种其他人类语言相关活动效率的计算机辅助系统而开发的系统中。然而，目前与该技术相关的两个主要限制。首先，它在说话者群体、说话风格、声环境和通信渠道的变化方面是脆弱的。其次，这项技术也被认为是昂贵的，其成本主要是开发新应用程序和新语言系统所需的大量转录语音数据。这个项目从几个方面解决了这些缺点。首先，将开发有效的模型表示和训练算法，以降低为新任务和新语言配置ASR系统的成本。它们将促进潜在的巨大但廉价的独立于应用程序的语音语料库与更小更昂贵的任务特定语音语料库一起使用，以配置ASR系统。设想的多语言场景是，来自多种语言的语音数据可以用于训练语音或文本语料库很少的语言的ASR系统。其次，将发展基于人类语音产生和语音感知模型的说话人归一化和声背景补偿方法。这将导致更有效的范例，以消除以前看不见的扬声器或声学环境的影响。最后，这些技术将应用于创建更健壮的基于人机界面的口语技术。主要目标是开发和评估支持技术，其主要动机是将基于文本的应用程序（例如在线搜索引擎）的功能扩展到口语界面。

Developments in automatic speech recognition (ASR) algorithms and implementations have resulted in a remarkable number of applications and services built around this technology. ASR technology appears in systems that have been developed for human-machine interaction, speech dictation, speech based indexing of audio and video resources, and computer aided systems for increasing the efficiency of various other human language related activities. However, there are two major limitations associated with the technology as it exists today. First, it is known to be fragile with respect to variation in speaker populations, speaking styles, acoustic environments, and communications channels. Second, the technology is also known to be expensive where the cost is dominated by the large quantities of transcribed speech data needed for developing systems for new applications and new languages. This project addresses these short-comings from several directions. First, efficient model representations and training algorithms will be developed to make the process of configuring ASR systems for new tasks and new language less expensive. They will facilitate scenarios where potentially huge but inexpensive application independent speech corpora can be used with much smaller more expensive task specific speech corpora to configure ASR systems. Multilingual scenarios are envisioned where speech data from many languages can be used for training an ASR system for languages where little speech or text corpora exist. Second, speaker normalization and acoustic background compensation approaches motivated by models of human speech production and speech perception will be developed. This will lead to more efficient paradigms for eliminating the effects of previously unseen speaker or acoustic environments. Finally, these techniques will be applied to creating more robust spoken language technology based human interfaces. The major goal is to develop and evaluate enabling technology with a major motivation being to extend the capabilities of text based applications like, for example, online search engines, to spoken language interfaces.