Context formalization and its use on dynamic adaptation of language model in ASR systems

Abstract

Speech interfaces have played an important role on the use of mobile devices and pervasive systems, as well as on the support for accessibility. However, Automatic Speech Recognition (ASR) systems still face several issues that limit their performance, specially due the variability and complexity of the speech communication. One of these issues concerns the presence and influence of context information. We argue that if, well handled, context information can significantly improve the speech understanding accuracy. This paper provides a formalization of context and proposes an approach to its use on the adaptation of language model in ASR systems. This proposal is applied to the development of a remote control to mobile robotic devices. Results show a decrease up to 2.67% and 3.3% on WER and WIL rates, respectively.