A hierarchical structure for modeling inter and intra phonetic information for phoneme recognition

In this paper, we present a two-layer hierarchical structure based on neural networks for phoneme recognition. The proposed structure attempts to model only the characteristics within a phoneme, i.e., intra-phonetic information. This differs from other state-of-the-art hierarchical structures where the first layer typically models the intra-phonetic information while the second layer focuses on modeling the contextual (inter-phonetic) information. An advantage of the proposed model is that it can be added to another layer that focuses on the inter-phonetic information. In this paper, we also show that the categorization between intra- and inter-phonetic information also allows to extend other state-of-the-art hierarchical approaches. A phoneme accuracy of 77.89% is achieved on the TIMIT database, which compares favorably to the best results obtained on this database.