Un algoritmo para la comprensión automática del habla sobre grafos de palabras

In this work we propose an algorithm for automatic speech understanding that takes a word graph as its input. First, this word graph is processed by means of a dynamic programming algorithm which gives as a result a second graph that includes semantic information. Computing the best path over this second graph allows us to obtain the most likely concept sequence, given the acoustic evidence reflected on the input word graph. As a result of the semantic decoding, the word sequence attached to the concept sequence as well as its semantic segmentation are also obtained.