Word-semantic lattices for spoken language understanding

The paper presents a method for converting word-based automatic speech recognition (ASR) lattices into word-semantic (W-SE) lattices that contain original words together with a partial semantic information - so-called semantic entities. Semantic entity detection algorithm generates semantic entities based on the expert-defined knowledge. The generated W-SE lattices have smaller vocabulary and consequently reduce the sparsity of the training data. The format of the W-SE lattices also naturally preserves the inherent uncertainty of the ASR output that can be exploited in subsequent dialog modules. The presented technique employs the framework of weighted finite state transducers which allows for efficient optimization of word-semantic lattices. We have evaluated the method in two different spoken language understanding tasks and obtained more than 10% reduction of concept error rate in comparison with using 1-best word hypothesis in both of those tasks.