Posterior features applied to speech recognition tasks with user-defined vocabulary

This paper presents a novel approach for those applications where vocabulary is defined by a set of acoustic samples. In this approach, the acoustic samples are used as reference templates in a template matching framework. The features used to describe the reference templates and the test utterances are estimates of phoneme posterior probabilities. These posteriors are obtained from a MLP trained on an auxiliary database. Thus, the speech variability present in the features is reduced by applying the speech knowledge captured by the MLP on the auxiliary database. Moreover, information theoretic dissimilarity measures can be used as local distances between features. When compared to state-of-the-art systems, this approach outperforms acoustic-based techniques and obtains comparable results to orthography-based methods. The proposed method can also be directly combined with other posterior-based HMM systems. This combination successfully exploits the complementarity between templates and parametric models.