Central China Normal University

Fujian Tq Digital Inc.

Fujian Netdragon Websoft Co., Ltd.

Fujian Tianquan Education Technology Limited

Dejian Liu

Zheng Zhong

Jian Xu

Yan Ge

Jing Peng

Ming Chen

Ronghua Wu

Zhenhua Fang

Guocheng Xu

Chen Lin

Disclosed are an emotional evolution method and terminal for a virtual avatar in educational metaverse. By collecting expression data and audio data of the virtual avatar, and performing emotional feature extraction based on the expression data and the audio data; fusing an extracted sound emotional feature with an extracted expression emotional feature by using an emotional feature fusion model, and performing emotion recognition on a multi-modal emotional feature fusion result obtained by fusion to obtain an emotional category corresponding to the multi-modal emotional feature fusion result; labeling the multi-modal emotional feature fusion result based on a semantic vector of the emotional category to generate an emotional evolution sequence; and driving the virtual avatar to perform emotional expression according to a target emotional evolution pattern extracted from the emotional evolution sequence, a unified and united multi-modal emotional feature fusion result is formed, the emotional self-evolution of the virtual avatar is achieved.

Emotional evolution method and terminal for virtual avatar in educational metaverse

Snow lion ornament

Disclosed are a semantic reasoning method and a terminal for a learning target in an education metaverse. The method includes receiving a description text of a learning target, segmenting the description text into a lexical item sequence, extracting an entry from the lexical item sequence, extracting a semantic lexical item based on the entry, and mapping the semantic lexical item into a first word vector. By capturing the semantic feature vector in the word vector, the subject and knowledge points in the learning target are obtained by reasoning, the target knowledge points of the learning target are determined and positioned, and the association relationship between the target knowledge points is obtained, so that the semantic information of the learning target can be fully mined, and the accuracy of the learning target extraction can be improved. It matches with the corresponding teaching scene based on the learning targets.

Semantic reasoning method and terminal for learning target in education metaverse

Disclosed are a method and a terminal for generating simulated voices of virtual teachers. Real voice samples of teachers are collected and converted into text sequences, and a text emotion polarity training set and a text tone training set are constructed according to the text sequences; a lexical item emotion model is constructed based on lexical items in the text sequences and is trained by using the emotion polarity training set, and word vectors, an emotion polarity vector, and a weight parameter are obtained by training; and the similarity between the word vector and the emotion polarity vector is calculated, and emotion features are extracted according to a similarity calculation result, a conditional vocoder is constructed according to the voice styles and emotion features to generate new voices with emotion changes. The method and the terminal contribute to satisfying the application requirements of high-quality virtual teachers.

Method and terminal for generating simulated voice of virtual teacher

Stylus

Growing community of inventors

Fuzhou, China

Dejian Liu