<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ricardo Cordoba</style></author><author><style face="normal" font="default" size="100%">Javier Ferreiros</style></author><author><style face="normal" font="default" size="100%">San Segundo, Ruben</style></author><author><style face="normal" font="default" size="100%">Javier Macias-Guarasa</style></author><author><style face="normal" font="default" size="100%">Juan Manuel Montero</style></author><author><style face="normal" font="default" size="100%">Fernando Fernandez</style></author><author><style face="normal" font="default" size="100%">Luis Fernando D'Haro</style></author><author><style face="normal" font="default" size="100%">Jose Manuel Pardo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Air traffic control speech recognition system cross-task &amp; speaker adaptation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Aerospace and Electronic Systems Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2006</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://geintra-uah.org/system/files/PublicadoDefinitivo-01705165.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Estados Unidos</style></pub-location><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">12-17</style></pages><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present an overview of the most comon techniques used in automatic speech recognition to adapt a general system to a different environment (known as cross-task adaptation) such as in air traffic control systems (ATC). The conditions present in ATC are very specific: very spontaneous, the presence of noise, and high speech speech. So, with a typical speech recognizer, the recognition results are unsatisfactory. We have to decide on the best option for the modeling: to develop acoustic models specific to those conditions from scratch using the data available for the new envirnoment, or to carry out cross-task adaptation starting from reliable &amp;nbsp;HMM&amp;nbsp;models (usually requiring less data in the target domain).&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><accession-num><style face="normal" font="default" size="100%">0.423</style></accession-num><call-num><style face="normal" font="default" size="100%">ENGINEERING, AEROSPACE</style></call-num></record></records></xml>