Speech-to-text translation by a non-word lexical unit based system

Speech understanding applications where a word based output of the uttered sentence is not needed, can benefit from the use of alternative lexical units. Experimental results from these systems show that the use of non-word lexical units bring us a new degree of freedom in order to improve the system performance (better recognition rate and lower size can be obtained in comparison to word based models). However, if the aim of the system is a speech-to-text translation, a post-processing stage must be included in order to convert the non-word sequences into word sentences. In this paper a technique to perform this conversion as well as an experimental test carried out over a task oriented Spanish corpus are reported. As a conclusion, we see that the whole speech-to-text system neatly outperforms the word-constrained baseline system.