CONF Lecorve_JEP_2012/IDIAP Impact du degre de supervision sur l'adaptation a un domaine d'un modele de langage a partir du Web Lecorvé, Gwénolé Dines, John Hain, Thomas Motlicek, Petr ASR Automatic Speech Recognition domain adaptation Language Models supervision Web data EXTERNAL http://publications.idiap.ch/attachments/papers/2012/Lecorve_JEP_2012.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/Lecorve_Idiap-RR-23-2012 Related documents Actes de la conference conjointe JEP-TALN-RECITAL 2012 Grenoble, France 1 193-200 2012 ATALA/AFCP in French Domain adaptation of a language model aims at re-estimating word sequence probabilities in order to better match the peculiarities of a given broad topic of interest. To achieve this task, a common strategy consists in retrieving adaptation texts from the Internet based on a given domain-representative seed text. In this paper, we study the influence of the choice of this seed text on the adaptation process and on the performances of adapted language models in automatic speech recognition. More precisely, the goal of this original study is to analyze the differences between supervised adaptation, in which the seed text is manually generated, and unsupervised adaptation, where the seed text is an automatic transcript. Experiments carried out on videos from a real-world use case mainly show that differences vary according to adaptation scenarios and that the unsupervised approach is globally convincing, especially according to its low cost. REPORT Lecorve_Idiap-RR-23-2012/IDIAP Impact du degre de supervision sur l'adaptation a un domaine d'un modele de langage a partir du Web Lecorvé, Gwénolé Dines, John Hain, Thomas Motlicek, Petr ASR Automatic Speech Recognition domain adaptation Language Models supervision Web data EXTERNAL http://publications.idiap.ch/attachments/reports/2012/Lecorve_Idiap-RR-23-2012.pdf PUBLIC Idiap-RR-23-2012 2012 Idiap July 2012 in French Domain adaptation of a language model aims at re-estimating word sequence probabilities in order to better match the peculiarities of a given broad topic of interest. To achieve this task, a common strategy consists in retrieving adaptation texts from the Internet based on a given domain-representative seed text. In this paper, we study the influence of the choice of this seed text on the adaptation process and on the performances of adapted language models in automatic speech recognition. More precisely, the goal of this original study is to analyze the differences between supervised adaptation, in which the seed text is manually generated, and unsupervised adaptation, where the seed text is an automatic transcript. Experiments carried out on videos from a real-world use case mainly show that differences vary according to adaptation scenarios and that the unsupervised approach is globally convincing, especially according to its low cost.