標(biāo)題: Titlebook: Dialogues with Social Robots; Enablements, Analyse Kristiina Jokinen,Graham Wilcock Book 2017 Springer Science+Business Media Singapore 201 [打印本頁(yè)] 作者: 遮陽(yáng)傘 時(shí)間: 2025-3-21 16:11
書目名稱Dialogues with Social Robots影響因子(影響力)
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書目名稱Dialogues with Social Robots網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Dialogues with Social Robots被引頻次
書目名稱Dialogues with Social Robots被引頻次學(xué)科排名
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書目名稱Dialogues with Social Robots讀者反饋
書目名稱Dialogues with Social Robots讀者反饋學(xué)科排名
作者: PLE 時(shí)間: 2025-3-21 22:01
Compact and Interpretable Dialogue State Representation with Genetic Sparse Distributed Memory for classification is adapted to the Reinforcement Learning task and a new way of building the prototypes is proposed. The approach is tested on a corpus of dialogues collected with an appointment scheduling system. The results are compared to a grid-based linear parametrisation. It is shown that l作者: ABASH 時(shí)間: 2025-3-22 04:12 作者: 無(wú)可非議 時(shí)間: 2025-3-22 07:06 作者: podiatrist 時(shí)間: 2025-3-22 09:05
How to Study a D.H. Lawrence Novel for classification is adapted to the Reinforcement Learning task and a new way of building the prototypes is proposed. The approach is tested on a corpus of dialogues collected with an appointment scheduling system. The results are compared to a grid-based linear parametrisation. It is shown that l作者: GEST 時(shí)間: 2025-3-22 13:43 作者: GEST 時(shí)間: 2025-3-22 18:54 作者: 萬(wàn)花筒 時(shí)間: 2025-3-22 21:39 作者: 滴注 時(shí)間: 2025-3-23 02:18
Compact and Interpretable Dialogue State Representation with Genetic Sparse Distributed Memoryialogue Systems (SDS) trained by Reinforcement Learning (RL) is often designed to reflect user satisfaction. To do so, the state space representation should be based on features capturing user satisfaction characteristics such as the mean speech recognition confidence score for instance. On the othe作者: 平庸的人或物 時(shí)間: 2025-3-23 09:15
Incremental Human-Machine Dialogue Simulation and test data-driven methods. We review the various simulator components in detail, including an unstable speech recognizer, and their differences with non-incremental approaches. Then, as an illustration of its capacities, an incremental strategy based on hand-crafted rules is implemented and comp作者: FLOAT 時(shí)間: 2025-3-23 10:08 作者: 免除責(zé)任 時(shí)間: 2025-3-23 14:31 作者: BOOST 時(shí)間: 2025-3-23 20:08 作者: 終點(diǎn) 時(shí)間: 2025-3-24 02:03
: A Simple Deep Reinforcement Learning Dialogue Systemnt learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, report that it is indeed possible to induce reasonable behaviours with such 作者: PLE 時(shí)間: 2025-3-24 04:25 作者: 不近人情 時(shí)間: 2025-3-24 09:49 作者: FEAT 時(shí)間: 2025-3-24 11:38
Natural Language Dialog System Considering Speaker’s Emotion Calculated from Acoustic Featuresalso enjoy non-task-oriented conversation with the computer. When an IVR system generates a response, it usually refers to just verbal information of the user’s utterance. However, when a person gloomily says “I’m fine,” people will respond not by saying “That’s wonderful” but “Really?” or “Are you 作者: 親屬 時(shí)間: 2025-3-24 18:48 作者: 軍火 時(shí)間: 2025-3-24 23:00 作者: 提煉 時(shí)間: 2025-3-25 03:00 作者: canonical 時(shí)間: 2025-3-25 05:43
Fisher Kernels on Phase-Based Features for Speech Emotion Recognitionn experience. This can be reached by speech emotion recognition, where the features are usually dominated by the spectral amplitude information while they ignore the use of the phase spectrum. In this chapter, we propose to use phase-based features to build up such an emotion recognition system. To 作者: Water-Brash 時(shí)間: 2025-3-25 10:13
https://doi.org/10.1007/978-981-10-2585-3IWSDS 2016; Interaction Technology; Human-Robot Dialogues; Dialogue State Tracking Challenge; Social Rob作者: carotenoids 時(shí)間: 2025-3-25 13:43
978-981-10-9659-4Springer Science+Business Media Singapore 2017作者: ligature 時(shí)間: 2025-3-25 16:06 作者: Coronary 時(shí)間: 2025-3-25 22:19 作者: Occipital-Lobe 時(shí)間: 2025-3-26 03:23 作者: 四目在模仿 時(shí)間: 2025-3-26 07:17
https://doi.org/10.1007/978-1-349-09125-6 the North Sami language in particular. The goal is to develop technological tools and resources that can be used for speech and language processing and for experimenting with interactive applications. Here we propose an interactive talking robot application as a means to reach these goals, and pres作者: Lignans 時(shí)間: 2025-3-26 09:56 作者: 赦免 時(shí)間: 2025-3-26 16:24 作者: 運(yùn)氣 時(shí)間: 2025-3-26 18:42 作者: ironic 時(shí)間: 2025-3-26 21:54
How to Study a Joseph Conrad Novelignificant human effort. To reduce this human effort, in this paper, we propose an active learning framework to construct example-based dialog systems efficiently. Specifically, we propose two uncertainty sampling strategies for selecting inputs to present to human annotators who create system respo作者: Intend 時(shí)間: 2025-3-27 04:09
How to Study a Joseph Conrad Novelions of the users. It is important that the questions are concise and concrete to prevent users from being annoyed. Our method selects the most appropriate question on the basis of expected utility calculated for four types of question: Yes/No, alternative, 3-choice, and Wh- questions. We define uti作者: 藝術(shù) 時(shí)間: 2025-3-27 08:47
How to Study a Joseph Conrad Novelerent facilities provided by each. Most of these toolkits include not only a method for representing states and actions, but also a mechanism for reasoning about and selecting the actions, often combined with a technical framework designed to simplify the task of creating end-to-end systems. This ne作者: Emmenagogue 時(shí)間: 2025-3-27 11:37
Coping with different kinds of novelnt learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, report that it is indeed possible to induce reasonable behaviours with such 作者: Fallibility 時(shí)間: 2025-3-27 15:30 作者: sulcus 時(shí)間: 2025-3-27 20:38 作者: MAG 時(shí)間: 2025-3-27 23:28
https://doi.org/10.1007/978-1-349-13279-9also enjoy non-task-oriented conversation with the computer. When an IVR system generates a response, it usually refers to just verbal information of the user’s utterance. However, when a person gloomily says “I’m fine,” people will respond not by saying “That’s wonderful” but “Really?” or “Are you 作者: 激勵(lì) 時(shí)間: 2025-3-28 06:00
How to Study: A Practical Guideemains there. In our research, we select the most essential features by applying a self-adaptive multi-objective genetic algorithm. The proposed approach is evaluated using data from different languages (English and German) with two different feature sets consisting of 37 and 384 dimensions, respect作者: 填滿 時(shí)間: 2025-3-28 07:32
https://doi.org/10.1007/978-1-349-13279-9uch a corpus is available, neither an agenda nor a statistically-based dialog control logic are options if the domain knowledge is broad. This article presents a module that automatically generates system-turn utterances to guide the user through the dialog. These system-turns are not established be作者: Insatiable 時(shí)間: 2025-3-28 10:28
https://doi.org/10.1007/978-3-030-45208-7eated by hand. In casual dialogues, the speaker sometimes asks his conversation partner questions about favorites or experiences. Since this behavior also appears in conversational dialogues with a dialogue system, systems must be developed to respond to such questions. However, the effectiveness of作者: GLIDE 時(shí)間: 2025-3-28 16:54 作者: 史前 時(shí)間: 2025-3-28 22:33 作者: Estimable 時(shí)間: 2025-3-29 02:44 作者: chemical-peel 時(shí)間: 2025-3-29 04:49
How to Study: A Practical Guideto generate a feature ranking. Based on this, a feature set for speech-based emotion recognition based on the most salient features has been created. By applying this feature set, we achieve a relative improvement of up?to 37.3?% without the need of time-consuming feature selection using a genetic algorithm.作者: 連累 時(shí)間: 2025-3-29 10:07 作者: 填滿 時(shí)間: 2025-3-29 12:38 作者: 形容詞 時(shí)間: 2025-3-29 17:54 作者: fastness 時(shí)間: 2025-3-29 21:45 作者: CT-angiography 時(shí)間: 2025-3-30 00:14
Entropy-Driven Dialog for Topic Classification: Detecting and Tackling Uncertaintyned information is improved through the dialog. The article focuses on the development and operation of this module, which is valid for agenda-based and statistical approaches, being applicable in both types of corpora.作者: Rinne-Test 時(shí)間: 2025-3-30 07:08
1876-1100 in research and development in the field of Spoken Dialogue .This book explores novel aspects of social robotics, spoken dialogue systems, human-robot interaction, spoken language understanding, multimodal communication, and system evaluation. It offers a variety of perspectives on and solutions to 作者: 玩笑 時(shí)間: 2025-3-30 11:56 作者: 貝雷帽 時(shí)間: 2025-3-30 12:47 作者: 文字 時(shí)間: 2025-3-30 18:40
Book 2017unication, and system evaluation. It offers a variety of perspectives on and solutions to the most important questions about advanced techniques for social robots and chat systems.?..Chapters by leading researchers address key research and development topics in the field of spoken dialogue systems, 作者: countenance 時(shí)間: 2025-3-30 20:54
Incremental Human-Machine Dialogue Simulationared to several non-incremental baselines. Their performances in terms of dialogue efficiency are presented under different noise conditions and prove that the simulator is able to handle several configurations which are representative of real usages.作者: 北極熊 時(shí)間: 2025-3-31 01:48
Active Learning for Example-Based Dialog Systemsnses for the selected inputs. We compare performance of these proposed strategies with a random selection strategy in simulation-based evaluation on 6 different domains. Evaluation results show that the proposed strategies are good alternatives to random selection in domains where the complexity of system utterances is low.
