Matrix Covariance Estimation methods for robust Security Speech Recognition with under-resourced conditions

The long term goal of our project is the development of robust Security Speech Recognition systems are based on Automatic Speech Recognition methodologies. The development of ASR systems involves dealing with issues such as Acoustic Phonetic Decoding (APD), Language Modelling (LM) or the development of appropriated Language Resources (LR). Thus these applications are generally very language-dependent and require very large resources. This work is focused to the selection of appropriated sub-word units with under-resourced and noisy conditions oriented to security tasks. The work has been carried out with a trilingual internet radio database. Thus, in order to decrease the negative impact that the lack of resources has in this issue we apply several data optimization methodologies based on Matrix Covariance Estimation and Ontology-based approaches.