CAGE
发表于 2025-3-23 12:15:21
Design of a Hungarian Emotional Database for Speech Analysis and Synthesis work on the subject is given. Next, the targeted applications of our emotional speech database are described. The problem of creating or collecting suitable prompts for different emotions and speaking styles is addressed. Then, we discuss the problem of collecting material for child tale reading. F
预示
发表于 2025-3-23 15:52:04
Coloring Multi-character Conversations through the Expression of Emotions pre-scripted scenes. This is done by using the same technique for emotion elicitation and computation that takes either input from the human author in the form of appraisal and dialog act tags or from a dialog planner in the form inferred emotion eliciting conditions. In either case, the system com
指令
发表于 2025-3-23 19:15:26
http://reply.papertrans.cn/16/1507/150622/150622_13.png
FAST
发表于 2025-3-24 01:47:20
Simulating the Emotion Dynamics of a Multimodal Conversational Agentus of the presented work lies on modeling a coherent course of emotions over time. The basic idea of the underlying emotion system is the linkage of two interrelated psychological concepts: an emotion axis – representing short-time system states – and an orthogonal mood axis that stands for an undir
Corral
发表于 2025-3-24 03:36:04
http://reply.papertrans.cn/16/1507/150622/150622_15.png
改正
发表于 2025-3-24 08:45:25
http://reply.papertrans.cn/16/1507/150622/150622_16.png
Consequence
发表于 2025-3-24 12:32:06
http://reply.papertrans.cn/16/1507/150622/150622_17.png
allergy
发表于 2025-3-24 16:45:09
Application of D-Script Model to Emotional Dialogue Simulationtive (emotional) processing of mass media texts and also applies to several other types of emotional communication including conflict, complaint and speech aggression. In the proposed model we distinguish rules for “rational” inference (r-scripts) and rules for “emotional” processing of meaning (d-s
DEMUR
发表于 2025-3-24 20:04:59
https://doi.org/10.1007/978-3-322-87095-7 train our system to recognise them. We also present a set of preliminary results which indicate that our neural net classifier is able to obtain accuracy rates of 96.6% and 89.9% for recognition of emotion arousal and valence respectively.
轻打
发表于 2025-3-25 01:43:55
http://reply.papertrans.cn/16/1507/150622/150622_20.png