A Review of Graphical Model Architectures for Speech Recognition
Graphs are a two-dimensional visual formalism that can be used to describe many different phenomena and are used in wide variety of fields including computer science, data and control flow, entity relationships and social networks, Petri and neural networks, software/hardware visualization, and parallel computation. The popularity of graphs is owing to their ability represent complex situations in an intuitive and visually appealing way. Statistical graphical models are a family of graphical abstractions of statistical models, where important aspects (e.g., factorization) of such models are represented using graphs. In recent years and due to a wide range of research, it has become apparent that graphical models offer a mathematically formal but widely flexible means for solving many of the problems in speech and language processing. Graphs are able to represent events at the very high level (such as relationships between linguistic classes), at the very low level (such as correlations between spectral features or acoustic landmarks), and at all levels in between (such as lexical pronunciation). A fundamental advantage of graphical models is rapidity — with a graphical model, it is possible to quickly express a novel complicated idea in an intuitive, concise, and mathematically precise way, and to speedily and visually communicate that idea between colleagues. Moreover, with the right software, it is possible to rapidly prototype that idea on a standard desktop workstation.