16 results about "Expressed emotion" patented technology

An automated image processing method for assessing facially-expressed emotions of an individual, the facially-expressed emotions being caused by operation of a vehicle, machinery, or robot by the individual, including operating a vehicle, machinery, or robot by the individual and thereby expose a vision of the individual to a stimulus, detecting non-verbal communication from a physiognomical expression of the individual based on image data by a first computer algorithm, the image data of the physiognomical expression of the individual being caused in response to the stimulus, assigning features of the non-verbal communication to different types of emotions by a second computer algorithm, analyzing the different types of emotions to determine an emotional state of mind of the individual, and generating at least one of a prompt, an alert, or a change in a setting of an operational parameter of the vehicle, based on the emotional state of mind of the individual.

In various examples, expressing emotion in speech for conversational AI systems and applications is described. Systems and methods are disclosed that use a machine learning model(s) (e.g., one or more large language models (LLMs)) to determine both an emotional state associated with speech output by a character and one or more values for one or more variables associated with the emotional state and / or the speech. For example, the variable(s) may include an intensity of the emotional state and / or a pitch, a rate, a volume, an emphasis, and / or the like of the speech. In some examples, the machine learning model(s) may determine the emotional state and / or the value(s) of the variable(s) using various types of inputs in addition to the text of the speech, such as user data and / or character data. The character may then output the speech in a way that expresses the emotional state.

The invention relates to a digital human face driving method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring a phoneme sequence corresponding to a to-be-broadcasted text of a target digital person; for any phoneme in the phoneme sequence, obtaining a mouth shape parameter and an emotion parameter corresponding to the phoneme; determining a control weight corresponding to the phoneme according to the mouth shape parameter and the emotion parameter; and driving the mouth shape of the target digital human according to the control weight corresponding to the phoneme. In the mouth shape motion curve calculation process of the digital human, the emotion information is aligned and fused, an emotional stylized mouth shape driving effect is generated, and the emotion is fully expressed while the mouth shape fitting accuracy is not influenced.

The present invention relates to an emotion-embedded learning method and system based on emotion knowledge, which is characterized in that it includes: aligning and integrating emotion words in a number of emotion lexicons to obtain an emotion space expressed by multi-dimensional vectors of each emotion word and the intensity value of each emotion word in the emotion space; performing embedded learning according to the emotion space expressed by the multi-dimensional vectors of each emotion word and the intensity value of each emotion word in the emotion space to obtain an improved word vector of each emotion word including context semantics and emotion semantics. The present invention adjusts the distribution positions of each other in the word semantic space according to the distance between words in the emotion space to achieve the purpose of integrating emotion semantics, and can be widely applied in the field of embedded learning.

The present invention is an audiovisual emotion classification method based on an improved ConvMixer network and dynamic focal loss, including: 1) collecting videos involving the human facial area expressing emotions, extracting image sequences and audio signals from the videos, and converting the audio signals into mel cepstral coefficient spectrograms; 2) constructing a ConvMixer network combined with an adjacency matrix, and extracting visual features using the ConvMixer network combined with the adjacency matrix; 3) extracting auditory features from the mel cepstral coefficient spectrograms using a ResNet34 network; 4) constructing a feature fusion and classification network for fusing visual features and auditory features, and performing emotion classification on each video according to the fused features; 5) training the network, and calculating the training loss through a focal loss function that fuses dynamic weights. It overcomes the problems of existing methods that focus on extracting local features of video frames while ignoring global features, and the loss function cannot make the model focus on difficult-to-separate samples, etc.

In various examples, expressing emotion in speech for conversational AI systems and applications is described herein. Systems and methods are disclosed that use one or more machine learning models (e.g., one or more language models) to determine one or more attributes associated with speech, such as one or more emotion attributes and / or one or more response attributes, and then use the attribute(s) to generate the speech that expresses emotion. In some examples, the machine learning model(s) may use various types of information to determine the attribute(s), such as user information, character information, a dialogue history, a current prompt, and / or so forth. For instance, using the information, the machine learning model(s) may determine one or more tags associated with emotional states and / or voice characteristics, where the tag(s) is then used to generate the speech in a voice that relates to the emotion.

An emotion recognition method includes the following steps: acquiring video data of a human-object interaction behavior process; performing data labeling on the positions of a person and an object and the interaction behaviors and emotions expressed by the person; constructing a feature extraction model based on deep learning, extracting features of interaction between the person and the object in a time-space dimension, and detecting the position and category of the human-object interaction behavior; mapping the detected interaction behavior category into a vector form through a word vector model; and finally, constructing a fusion model based on deep learning, fusing the interaction behavior vector and the time-space interaction behavior features, and identifying the emotion expressed by the interaction person.

To express emotions that are more lifelike. [Solution] The robot 100 is equipped with a control unit 110 that acquires external information using sensors, acquires the internal state of the robot body, sets a third emotion determined based on a first emotion or a first emotion change degree determined based on the external information and a second emotion or a second emotion change degree determined based on the internal state, as a simulated emotion of the robot 100, and controls the operation of the robot 100 based on the set simulated emotion.

The invention relates to the technical field of natural language processing, and particularly discloses a role playing ability optimization method and device of a large language model, equipment and a medium. Performing dialogue recognition and processing on the novel text to obtain role playing data; performing supervised all-parameter fine tuning on the basis of the role playing data to obtain an initial fine tuning model, and reasoning dialogue input data in the data to obtain model reasoning data; and performing iterative optimization based on the role playing data and the model reasoning data to obtain target model parameters, and generating a target large language model. According to the method, the role playing data is extracted from the novel text, so that the role playing data comprises spoken and stylized data in various scenes, emotion can be accurately expressed, story development is promoted, iterative optimization is performed according to the role playing data, and the user experience is improved. Therefore, the model can learn oral and stylized expression ability, emotional expression and plot promotion ability from role playing data, and the role playing ability of the large language model is improved.

To collect emotion data of a high degree of precision to which an accurate emotion expression timing and an accurate emotion type are applied, and improve accuracy of emotion estimation.SOLUTION: An emotion data collection system for collecting emotion data associating an ambient environment of a subject with an emotion type expressed by the subject includes: an emotion data acquisition device for detecting an emotion expression timing; and an emotion data connection device for associating the ambient environment data with the emotion type data. The emotion data acquisition device receives an input operation for the emotion expression timing, and stores the received emotion expression timing. The emotion data connection device notifies the ambient environment at the stored emotion expression timing, receives an input operation for the emotion type for the notified ambient environment, and associates and stores the ambient environment at the emotion expression timing and the received emotion type.SELECTED DRAWING: Figure 10

An object of the system according to the embodiment is to allow a user to anonymously express an emotion and receive mental health care without anxiety.SOLUTION: A system according to an embodiment includes an emotion expression unit, a virtual space providing unit, a color scheme customization unit, a history deletion unit, and a mental health service providing unit. The emotion expression unit anonymously expresses an emotion. The virtual space providing unit processes the feeling expressed by the feeling expression unit. The color scheme customizing unit customizes the color scheme of the virtual space provided by the virtual space providing unit. The history deletion part deletes the history of the feeling expressed by the feeling expression part. The mental health service providing unit provides a professional mental health service based on the emotion expressed by the emotion expression unit.SELECTED DRAWING: Figure 1

The present disclosure relates to the recognition of emotion in speech, both what is said and how it is said and the detection of a possible concordance or discrepancy between the two. A method is described for generating a time-stamped history of emotions a user reports by voice along with emotions detected automatically from those voice reports. A user utterance is analyzed using speech-to-text processing and a natural-language processing model to determine the emotion the user reports feeling. The user utterance is also analyzed using acoustic analysis to detect the emotion expressed in the user's voice report. A harmony report is generated from the time-stamped reports of concordance and discrepancy to measure the extent to which the user's perception of their emotions agrees with the emotions detected. The purpose of the invention is to provide insight into a user's emotions in real time and over time.

The invention discloses a data preprocessing method and device for text emotional tendency analysis, and belongs to the technical field of computers. The method comprises the following steps: performing statement segmentation processing on target text data to obtain a first target statement; for each first target statement, based on a pre-stored emotional tendency dictionary library, analyzing whether the first target statement contains a phrase of a first target structure or a phrase of a second target structure; performing rewriting operation on the first target statement containing the phrase of the first target structure, and / or performing removal operation on the first target statement containing the phrase of the second target structure, the condition that the use words in the first target statement are opposite to the actual expression semantics of the first target statement or the emotion and evaluation expressed by the first target statement are indefinite is eliminated. According to the data preprocessing method for text emotional tendency analysis, the accuracy of text emotional tendency analysis can be improved.

The invention discloses an euler space-based emotion continuous space representation method. The euler space-based emotion continuous space representation method comprises the following steps of: converting emotion vocabularies: converting emotion related vocabularies into tokens which can be processed by a language model; vector embedding generation: converting emotional vocabularies into high-dimensional embedded vectors by using a pre-trained large language model, and representing distribution characteristics of emotions in a semantic space; vector dimension reduction processing: mapping the high-dimensional embedded vector to a two-dimensional plane space by using a dimension reduction algorithm; and euler space conversion and visualization: in the two-dimensional space after dimension reduction, defining the wake-up degree as a radius r and the pleasure degree as an angle theta, constructing euler space representation of emotions on the basis, and displaying emotional point distribution through a visualization tool, the wake-up degree is represented by the radius r, the pleasure degree is represented by the angle theta, and the emotional point distribution is displayed through a visual tool. And meanwhile, an emotion distribution model better conforming to human cognition is constructed by mining the relationship between emotion vectors generated by a large language model, and the correlation and distribution characteristics between different emotions can be reflected more accurately.

The invention discloses an active listening emotion support reply generation method and system oriented to implicit demand perception, relates to the technical field of natural language processing, and aims to solve the problem that an existing method usually only pays attention to identification and pacification of dominant emotions of a user and lacks in-depth understanding and active response ability to implicit demands. In order to solve the problem of difficulty in obtaining accurate emotion support reply due to the fact that the emotion support reply is difficult to obtain, the invention applies to gradually mine deep psychological causes of a user and extract potential demand information by performing multi-level reasoning on emotion expression and semantic clues input by the user. The overall framework comprises a listening stage and a suggestion stage, the listening stage guides a user to more truly express emotion and intention through an active listening strategy, and the suggestion stage generates targeted and common emotion support reply according to the identified implicit demand. According to the method, dynamic processing from surface emotion to deep demand is realized in combination with an inference chain mechanism, and the method has continuous emotional sharing ability and personalized response characteristics. And accurate emotion support reply can be obtained.

The utility model discloses a pain scoring ruler, which belongs to the technical field of medical auxiliary equipment, and comprises a scoring ruler main body, a scoring device, a language playing device and a self-drawing expression device, the language playing device comprises a vocal apparatus and a plurality of electrode contacts, the vocal apparatus is electrically connected with the plurality of electrode contacts, and the vocal apparatus is electrically connected with the plurality of electrode contacts. The scoring device comprises an expression scoring assembly and a scoring scale assembly, and the color of a scoring plate is gradually changed from light color to dark color in the scoring scale direction; according to the device, through the scoring device, a patient can quickly and visually understand and identify the general degree of the pain degree, and meanwhile, the device is suitable for young children and patients who cannot report by themselves and are scored through behavioral observation, so that the patient can more easily understand or judge the severity of the pain in combination with common sense of life; the device is also suitable for illiterate and expression patients through a language playing device, and the pain degree can be understood by pressing corresponding pain values to carry out language broadcasting.