Genetic triangulation of graphical models for speech and language processing

Graphical models are an increasingly popular approach for speech and language processing. As researchers design ever more complex models it becomes crucial to find triangulations that make inference problems tractable. This paper presents a genetic algorithm for triangulation search that is well-suited for speech and language graphical models. It is unique in two ways: First, it can find triangulations appropriate for graphs with a mix of stochastic and deterministic dependencies. Second, the search is guided by optimizing the inference speed (CPU runtime) on real data. We show results on 10 real-world speech and language graphs and demonstrate inference speed-ups over standard triangulation methods.