Efficient configuration of robust spoken language technology based human interfaces

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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.