A unified framework for translation and understanding allowing discriminative joint decoding for multilingual speech semantic interpretation. (January 2016)

• Record Type: Journal Article
• Authors: Jabaian, Bassam; Lefèvre, Fabrice; Besacier, Laurent
• Is Part Of: Computer speech & language. Volume 35(2016)
• Page Start: 185
• Page End: 199
• Publication Date: 2016-01
• Subjects: Multilingual speech understanding -- Conditional random fields -- Hypothesis graphs -- Statistical machine translation -- Dialogue systems; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2014.06.003
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: A comparison between the methods used for speech translation and understanding. A unified framework for translation and understanding. A discriminative joint decoding for multilingual speech semantic interpretation. The proposition is competitive with state-of-the-art techniques. The framework can be generalized to other components of a dialogue system. Abstract: Probabilistic approaches are now widespread in most natural language processing applications and selection of a particular approach usually depends on the task at hand. Targeting speech semantic interpretation in a multilingual context, this paper presents a comparison between the state-of-the-art methods used for machine translation and speech understanding. This comparison justifies our proposition of a unified framework for both tasks based on a discriminative approach. We demonstrate that this framework can be used to perform a joint translation-understanding decoding which allows to combine, in the same process, translation and semantic tagging scores of a sentence. A cascade of finite-state transducers is used to compose the translation and understanding hypothesis graphs (1-bests, word graphs or confusion networks). Not only this proposition is competitive with the state-of-the-art techniques but also its framework is even more attractive as it can be generalized to other components of human–machine vocal interfaces (e.g. speech recognizer) so as to allow a richer transmission of information between them.