A new technique based on augmented language models to improve the performance of spoken dialogue systems

Abstract

This paper presents a new technique that aims to improve the performance of spoken dialogue systems by using the so-called augmented language models. We define an augmented language model as a compound of a language model and a set of values concerning parameters that can influence the speech recognition when the language model is used. The diverse language models used by a dialogue system can be very different, in terms of perplexity for example. Then, the aim of the technique is to find and use the combination of values concerning the different parameters that leads to the best recognition results when the different language models are used by a dialogue system. The technique has been applied to a dialogue system for the fast food domain. The results show that when the augmented language models are used the system’s performance is enhanced. In the experiments we have achieved a reduction of 9,33% in the word error rate and an increment of 11,26% in the sentence understanding.