User-Adaptive A Posteriori Restoration for Incorrectly Segmented Utterances in Spoken Dialogue Systems

Ideally, the users of spoken dialogue systems should be able to speak at their own tempo. Thus, the systems needs to interpret utterances from various users correctly, even when the utterances contain pauses. In response to this issue, we propose an approach based on a posteriori restoration for incorrectly segmented utterances. A crucial part of this approach is to determine whether restoration is required. We use a classification-based approach, adapted to each user. We focus on each user’s dialogue tempo, which can be obtained during the dialogue, and determine the correlation between each user’s tempo and the appropriate thresholds for classification. A linear regression function used to convert the tempos into thresholds is also derived. Experimental results show that the proposed user adaptation approach applied to two restoration classification methods, thresholding and decision trees, improves classification accuracies by 3.0% and 7.4%, respectively, in cross validation

User Impressions of System Questions to Acquire Lexical Knowledge during Dialogues

We have been addressing the problem of acquiring attributes of unknown terms through dialogues and previously proposed an approach using the implicit confirmation process. It is crucial for dialogue systems to ask questions that do not diminish the user’s willingness to talk. In this paper, we conducted a user study to investigate user impression for several question types, including explicit and implicit, to acquire lexical knowledge. We clarified the order among the types and found that repeating the same question type annoys the user and degrades user impression even when the content of the questions is correct. We also propose a method for determining whether an estimated attribute is correct, which is included in an implicit question. The method exploits multiple-user responses to implicit questions about the attribute of the same unknown term. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly estimated attributes than when only single-user responses were considered

Learning to describe multimodally from parallel unimodal data? A pilot study on verbal and sketched object descriptions

Han T, Zarrieß S, Komatani K, Schlangen D. Learning to describe multimodally from parallel unimodal data? A pilot study on verbal and sketched object descriptions. In: Proceedings of the 22nd Workshop on the Semantics and Pragmatics of Dialogue (AixDial). 2018

Introduction for speech and language for interactive robots

This special issue includes research articles which apply spoken language processing to robots that interact with human users through speech, possibly combined with other modalities. Robots that can listen to human speech, understand it, interact according to the conveyed meaning, and respond represent major research and technological challenges. Their common aim is to equip robots with natural interaction abilities. However, robotics and spoken language processing are areas that are typically studied within their respective communities with limited communication across disciplinary boundaries. The articles in this special issue represent examples that address the need for an increased multidisciplinary exchange of ideas

ドメイン ヒイソンナ タイワ センリャク オ ソナエタ ジョウホウ ケンサク オンセイ タイワ システム

京都大学0048新制・課程博士博士(情報学)甲第9846号情博第61号新制||情||16(附属図書館)UT51-2003-B386京都大学大学院情報学研究科知能情報学専攻(主査)教授 奥乃 博, 教授 田中 克己, 教授 石田 亨学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA