6,212 research outputs found
Conversational Agents, Humorous Act Construction, and Social Intelligence
Humans use humour to ease communication problems in human-human interaction and \ud
in a similar way humour can be used to solve communication problems that arise\ud
with human-computer interaction. We discuss the role of embodied conversational\ud
agents in human-computer interaction and we have observations on the generation\ud
of humorous acts and on the appropriateness of displaying them by embodied\ud
conversational agents in order to smoothen, when necessary, their interactions\ud
with a human partner. The humorous acts we consider are generated spontaneously.\ud
They are the product of an appraisal of the conversational situation and the\ud
possibility to generate a humorous act from the elements that make up this\ud
conversational situation, in particular the interaction history of the\ud
conversational partners
Semi-Supervised Speech Emotion Recognition with Ladder Networks
Speech emotion recognition (SER) systems find applications in various fields
such as healthcare, education, and security and defense. A major drawback of
these systems is their lack of generalization across different conditions. This
problem can be solved by training models on large amounts of labeled data from
the target domain, which is expensive and time-consuming. Another approach is
to increase the generalization of the models. An effective way to achieve this
goal is by regularizing the models through multitask learning (MTL), where
auxiliary tasks are learned along with the primary task. These methods often
require the use of labeled data which is computationally expensive to collect
for emotion recognition (gender, speaker identity, age or other emotional
descriptors). This study proposes the use of ladder networks for emotion
recognition, which utilizes an unsupervised auxiliary task. The primary task is
a regression problem to predict emotional attributes. The auxiliary task is the
reconstruction of intermediate feature representations using a denoising
autoencoder. This auxiliary task does not require labels so it is possible to
train the framework in a semi-supervised fashion with abundant unlabeled data
from the target domain. This study shows that the proposed approach creates a
powerful framework for SER, achieving superior performance than fully
supervised single-task learning (STL) and MTL baselines. The approach is
implemented with several acoustic features, showing that ladder networks
generalize significantly better in cross-corpus settings. Compared to the STL
baselines, the proposed approach achieves relative gains in concordance
correlation coefficient (CCC) between 3.0% and 3.5% for within corpus
evaluations, and between 16.1% and 74.1% for cross corpus evaluations,
highlighting the power of the architecture
Affect-LM: A Neural Language Model for Customizable Affective Text Generation
Human verbal communication includes affective messages which are conveyed
through use of emotionally colored words. There has been a lot of research in
this direction but the problem of integrating state-of-the-art neural language
models with affective information remains an area ripe for exploration. In this
paper, we propose an extension to an LSTM (Long Short-Term Memory) language
model for generating conversational text, conditioned on affect categories. Our
proposed model, Affect-LM enables us to customize the degree of emotional
content in generated sentences through an additional design parameter.
Perception studies conducted using Amazon Mechanical Turk show that Affect-LM
generates naturally looking emotional sentences without sacrificing grammatical
correctness. Affect-LM also learns affect-discriminative word representations,
and perplexity experiments show that additional affective information in
conversational text can improve language model prediction
Follow-up question handling in the IMIX and Ritel systems: A comparative study
One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it
Affective games:a multimodal classification system
Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation
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