On the dynamic adaptation of language models based on dialogue information

Lucas Cuesta, Juan Manuel; Ferreiros López, Javier; Fernández Martínez, Fernando; Echeverry Correa, Julian David y Lebai Lutfi, Syaheerah Binti (2012). On the dynamic adaptation of language models based on dialogue information. "Expert Systems with Applications", v. 40 (n. 4); pp. 1069-1085. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2012.08.029.

Descripción

Título: On the dynamic adaptation of language models based on dialogue information
Autor/es:
  • Lucas Cuesta, Juan Manuel
  • Ferreiros López, Javier
  • Fernández Martínez, Fernando
  • Echeverry Correa, Julian David
  • Lebai Lutfi, Syaheerah Binti
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert Systems with Applications
Fecha: Marzo 2012
Volumen: 40
Materias:
Palabras Clave Informales: Spoken dialogue system; Speech recognition; Language models; Dynamic adaptation; Semantic clustering; Dialogue-based information
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (8MB) | Vista Previa

Resumen

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks.

Más información

ID de Registro: 22672
Identificador DC: http://oa.upm.es/22672/
Identificador OAI: oai:oa.upm.es:22672
Identificador DOI: 10.1016/j.eswa.2012.08.029
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0957417412009906
Depositado por: Memoria Investigacion
Depositado el: 03 Mar 2014 18:23
Ultima Modificación: 21 Abr 2016 17:40
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM