Exploring PPRLM performance for NIST 2005 Language Recognition Evaluation
Alberto Montero-Asenjo, Doroteo T. Toledano, Javier Gónzalez-Domínguez, Joaquín González-Rodríguez, Javier Ortega-García · 2006
In the language recognition area parallel phone recognition followed by language modelling (PPRLM) is one the most widespread approaches. Although all PPRLM systems are based on the same ideas, the performance achieved by such systems depends heavily on multiple design parameters that have to be defined. As part of our preparation for the 2005 NIST Language Recognition Evaluation we have explored the effect of some of these parameters. Some of them are very common in the design of PPRLM systems, such as the number of underlying phonetic recognisers, the normalisations used or the amount of training data available. Others, like the possibility of using unlabelled speech to train phonetic recognisers or changing the complexity of the phonetic recognisers are less common and provide ways to achieve slight improvements without more labelled speech