65,582 research outputs found
New Technique to Enhance the Performance of Spoken Dialogue Systems by Means of Implicit Recovery of ASR Errors
This paper proposes a new technique to implicitly correct some ASR
errors made by spoken dialogue systems, which is implemented at two levels:
statistical and linguistic. The goal of the former level is to employ for the correction
knowledge extracted from the analysis of a training corpus comprised of
utterances and their corresponding ASR results. The outcome of the analysis is
a set of syntactic-semantic models and a set of lexical models, which are optimally
selected during the correction. The goal of the correction at the linguistic
level is to repair errors not detected during the statistical level which affects the
semantics of the sentences. Experiments carried out with a previouslydeveloped
spoken dialogue system for the fast food domain indicate that the
technique allows enhancing word accuracy, spoken language understanding and
task completion by 8.5%, 16.54% and 44.17% absolute, respectively.Ministerio de Ciencia y Tecnología TIN2007-64718 HAD
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
Sequential Dialogue Context Modeling for Spoken Language Understanding
Spoken Language Understanding (SLU) is a key component of goal oriented
dialogue systems that would parse user utterances into semantic frame
representations. Traditionally SLU does not utilize the dialogue history beyond
the previous system turn and contextual ambiguities are resolved by the
downstream components. In this paper, we explore novel approaches for modeling
dialogue context in a recurrent neural network (RNN) based language
understanding system. We propose the Sequential Dialogue Encoder Network, that
allows encoding context from the dialogue history in chronological order. We
compare the performance of our proposed architecture with two context models,
one that uses just the previous turn context and another that encodes dialogue
context in a memory network, but loses the order of utterances in the dialogue
history. Experiments with a multi-domain dialogue dataset demonstrate that the
proposed architecture results in reduced semantic frame error rates.Comment: 8 + 2 pages, Updated 10/17: Updated typos in abstract, Updated 07/07:
Updated Title, abstract and few minor change
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