State of the Art in Statistical Methods for Language and Speech Processing

Recent years have seen rapid growth in the deployment of statistical methods for computational language and speech processing. The current popularity of such methods can be traced to the convergence of several factors, including the increasing amount of data now accessible, sustained advances in computing power and storage capabilities, and ongoing improvements in machine learning algorithms. The purpose of this contribution is to review the state of the art in both areas, point out the top trends in statistical modelling across a wide range of problems, and identify their most salient characteristics. The paper concludes with some prognostications regarding the likely impact on the field going forward.