On the dynamic adaptation of language models based on dialogue information

Lucas Cuesta, Juan Manuel and Ferreiros López, Javier and Fernández Martínez, Fernando and Echeverry Correa, Julian David and 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.

Description

Title: On the dynamic adaptation of language models based on dialogue information
Author/s:
  • Lucas Cuesta, Juan Manuel
  • Ferreiros López, Javier
  • Fernández Martínez, Fernando
  • Echeverry Correa, Julian David
  • Lebai Lutfi, Syaheerah Binti
Item Type: Article
Título de Revista/Publicación: Expert Systems with Applications
Date: March 2012
ISSN: 0957-4174
Volume: 40
Subjects:
Freetext Keywords: Spoken dialogue system; Speech recognition; Language models; Dynamic adaptation; Semantic clustering; Dialogue-based information
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 22672
DC Identifier: http://oa.upm.es/22672/
OAI Identifier: oai:oa.upm.es:22672
DOI: 10.1016/j.eswa.2012.08.029
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417412009906
Deposited by: Memoria Investigacion
Deposited on: 03 Mar 2014 18:23
Last Modified: 21 Apr 2016 17:40
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