Efficient configuration of robust spoken language technology based human interfaces

自动语音识别（ASR）算法和实现的发展已经导致了大量围绕该技术构建的应用程序和服务。 ASR技术出现在已经开发用于人机交互、语音听写、基于语音的音频和视频资源的索引的系统中，以及用于提高各种其他人类语言相关活动的效率的计算机辅助系统中。 然而，有两个主要的限制与技术，因为它存在的今天。 首先，它是已知的是脆弱的，在扬声器人口，说话风格，声学环境和通信信道的变化。 第二，该技术也被认为是昂贵的，其中成本由开发用于新应用和新语言的系统所需的大量转录语音数据支配。 本项目从几个方面解决这些短期问题。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系统的过程的成本。 它们将促进这样的场景，即潜在的巨大但廉价的独立于应用程序的语音语料库可以与小得多的更昂贵的特定于任务的语音语料库一起使用，以配置ASR系统。 设想了多语言场景，其中来自许多语言的语音数据可以用于训练用于存在很少语音或文本语料库的语言的ASR系统。第二，说话人规范化和声学背景补偿的方法，激励人类的语音生产和语音感知模型将被开发。 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，这些技术将被应用于创建更强大的基于口语技术的人机界面。 主要目标是开发和评估使能技术，其主要动机是将基于文本的应用程序的功能扩展到口语界面，例如在线搜索引擎。

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.