1937 results about "Speech control" patented technology

Disclosed in the present invention are a multi-modal shared teleoperation system and method for a three-arm space robot. The system at least comprises a master-side teleoperation system, a communication module and a slave-side robot system, wherein the master-side teleoperation system at least comprises two force feedback hand controllers, a microphone array and upper computer software, and the slave-side robot system comprises two operating arms each equipped with a gripper at the end, an observation arm having a binocular camera mounted at the end, a vision unit, a force sensor and lower computer software. The method comprises: an operator controlling two operating arms of an extravehicular robot to execute a task, and controlling an observation arm to acquire a better local field of view. In the method, a multi-modal teleoperation method comprising pose control, voice control and force control is fused with autonomous control of a robot by means of a shared control algorithm, and thus, human-robot collaborative control over the position, orientation and contact force of a robotic arm can be realized on the basis of the requirements of the operator, and the robot autonomously executes other relatively simple tasks, thereby reducing the operation burden of operators, and improving the control efficiency.

A multimodal shared telerobotic system and method for a three-arm space robot, the system at least includes a local-site system, a communication module, and a remote-site system, where the local-site system includes two force-feedback haptic devices for left and right hands, a microphone array, and upper computer software; the remote-site system includes two robotic arms provided with end-effectors, an observation arm with a stereo camera installed at an end thereof, two force sensors, a vision unit and lower computer software; an operator can control the two robotic arms of the robot outside a cabin for performing operations, and control the observation arm to obtain a better local view; and a multimodal telerobotic control method of pose control, voice control, and force control is integrated with the robot's autonomous control through a shared control algorithm.

An IoT-based intelligent room management system for hotels and resorts, consisting of: a variety of IoT-enabled devices in individual hotel rooms, with each device configured to control a specific room function, including lighting, air conditioning, entertainment systems, and security systems; a centralized management platform that is operationally coupled with the multitude of IoT-enabled devices and configured to communicate with the devices via a wireless network, with the centralized management platform collecting real-time data on room settings, occupancy status, and device performance; a guest interface that includes a mobile application or voice-activated assistant and is configured to allow guests to customize and control room functions, with the guest interface communicating with the central management platform to implement changes to room settings according to user preferences; An automatic occupancy detection system integrated into IoT-enabled devices and the central management platform. The system detects the presence or absence of guests in the room and adjusts room settings, including lighting and air conditioning, to optimize energy consumption when the room is unoccupied; a keyless access system that is operationally linked to the central management platform and provides guests with access to their assigned rooms via smartphones or wearable devices using wireless communication protocols; and A resource management module integrated into the central management platform, providing real-time insights into room occupancy, energy consumption and equipment status, enabling predictive maintenance and optimizing resource allocation within the hotel or resort.

The embodiment of the invention provides a method, a system and equipment for voice control of equipment based on a large language model. The method is applied to the field of electric data processing, and comprises the following steps: collecting a user voice signal, and preprocessing the voice signal to obtain a corresponding text through voice recognition; performing entropy calculation on the equipment control instruction set, the attribute set and the attribute value set to obtain one or more key attributes, and constructing a cue word example; and constructing a complete cue word in combination with the corresponding text and the cue word example, and inputting the complete cue word into the large language model to obtain an equipment control instruction. Through the scheme disclosed by the invention, a voice control task with long context, multiple languages and generalization requirements can be completed.

The invention discloses a voice control implementation method and system based on a large model, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of insufficient recognition precision, low response speed and limited complex context processing capability in voice control. Comprising the following steps: analyzing a user voice instruction into a text instruction through a voice recognition model; performing semantic feature extraction on an input text instruction through a pre-trained text embedding model, and outputting a text embedding vector as a to-be-recognized text embedding vector; searching whether a text embedding vector similar to the to-be-identified text embedding vector exists in a pre-constructed vector database, if not, executing text analysis, and if yes, returning corresponding control information from the vector database; and taking the text instruction and the question statement as input, performing semantic analysis through a pre-trained large language model to obtain control information, and storing a text embedding vector corresponding to the current user voice instruction and the control information into a vector database.

The invention belongs to the technical field of sound equipment control, and particularly relates to an intelligent sound equipment control method and system and a storage medium, and the method enables the interaction control of the intelligent sound equipment on home equipment to be based on the control effect, and can screen out the interaction control record with the abnormal control effect based on the historical interaction control record. According to the method and the device, the voice control instruction is subjected to abnormal traceability and voice control instruction complexity correlation analysis, so that the targeted adjustment direction of the sound interaction control is obtained, feedback closed-loop adjustment of the sound interaction control is realized, the blank of the existing sound interaction control on feedback control is effectively filled, and the user experience is improved. Continuous optimization and improvement of sound interaction control are accelerated and promoted to a certain extent, so that the user experience is improved continuously, and sustainable development of the intelligent sound is facilitated.

The invention provides a practical training method and system based on multi-modal Internet of Things perception and virtual-real symbiosis, and relates to the technical field of virtual-real symbiosis, and the method comprises the steps: collecting the multi-modal perception data of a practical training environment through a plurality of heterogeneous Internet of Things devices arranged in the practical training environment; constructing a three-dimensional digital twinborn model of the practical training environment according to the category and position information of the object in the practical training environment, the voice control instruction and the comprehensive environment parameters; the method comprises the following steps: setting training items and evaluation indexes in a virtual-real symbiotic training space, receiving a training item selection instruction input by training personnel, and loading a corresponding training item according to the selection instruction, and sending a control instruction of the practical training project to a virtual equipment model in the three-dimensional digital twinborn model and Internet of Things equipment mapped with the virtual equipment model in the practical training environment for practical training, and evaluating the practical training process and the practical training result of the practical training project according to the evaluation index after the practical training project is finished. And a training evaluation report is generated and fed back to the training management system.

An interactive method based on a large model, a training method, and an intelligent agent, which relate to fields of artificial intelligence, speech recognition, speech interaction, deep learning, large models, and application scenarios of knowledge search, autonomous driving, intelligent customer service, intelligent speech control, smart e-commerce, AI healthcare. The interactive method includes: acquiring a request speech; performing a speech recognition on the request speech to obtain a speech recognition feature representing a request semantics; and processing the speech recognition feature using the large model to obtain a response text, where the response text includes response words arranged in sequence, a target response word among the response words is determined by processing the speech recognition feature and an associated response word feature using an attention fusion layer of the large model, and the associated response word feature is related to an associated response word arranged before the target response word.

According to the embodiment of the invention, a voice processing method and device, equipment and a medium are provided. The method comprises the steps that wake-up word voiceprint features of wake-up word voice segments are extracted, the wake-up word voice segments are matched with wake-up words, and the wake-up word voiceprint features indicate a target speaker; dividing the target voice into at least one voice segment, wherein the target voice is collected after the wake-up word voice segment; extracting at least one voiceprint feature of the at least one voice segment; based on the similarity between the at least one voiceprint feature and the wake-up word voiceprint feature, determining whether the at least one voice segment is from a target speaker; and determining instruction word recognition of the at least one voice segment based on a determination result of whether the at least one voice segment is from the target speaker. Therefore, noise interference or voice interference of others can be reduced in voice wake-up and voice control applications.

The invention relates to the technical field of intelligent control, in particular to a limo equipment wireless intelligent control method and system based on AI and voice recognition. The method comprises the following steps: a user sends a voice command, denoising and enhancing voice signals through a beam forming technology and an adaptive filter, enhancing a time-frequency structure of the voice signals by using Fourier transform and dynamic gain, and performing auditing verification by generating an auditing code; extracting multi-modal features, generating high-dimensional hidden representation through a bidirectional long-short term memory network and an attention mechanism after fusion, optimizing text output, and improving text quality by using spelling error correction and semantic consistency verification; then analyzing the instruction and generating a standardized instruction format through an intention recognition and slot filling model, executing rule verification to ensure the reasonability and safety of the instruction, and finally generating a control command in combination with environmental data to ensure the intelligent control of equipment. According to the invention, efficient and intelligent voice control and safety verification of the motor home equipment are realized.

The invention relates to the technical field of vehicle lamp systems, in particular to an intelligent vehicle lamp system based on DLP projection, a control method and a vehicle. The intelligent vehicle lamp system based on DLP projection comprises an optical system, an intelligent control system, an execution module and a power management system, the system collects environment data in real time through a sensor, a central processing unit fuses multi-source information and generates a control instruction through an AI algorithm, and the light source brightness, the irradiation angle and the road surface projection content are dynamically adjusted; meanwhile, voice interaction and remote control are supported, and the self-adaptive lighting and safety warning functions are achieved. Through the intelligent control and DLP projection technology, the vehicle lamp performance is improved, the lighting effect is optimized, and the night driving safety is enhanced; self-adaptive adjustment is realized, and the driving experience is improved; the energy consumption is reduced by more than 30%; the battery life is prolonged; v2X communication is supported, and intelligent early warning is realized; a voice control function is provided, and operation is more convenient and safer; a road surface projection function is added, navigation arrows and warning marks can be displayed, and visual driving assistance is provided.

The invention discloses a vehicle-mounted VR back-row entertainment method, and relates to the field of VR, and the method comprises the steps: processing image data transmitted by a controller through a DCT plate negative reflection plate lens, and generating aerial imaging data; reflecting a virtual image corresponding to the aerial imaging data by using a plane mirror to generate real image data; performing voice recognition processing on the input voice of the user to obtain voice control instruction data; acquiring image data of user gestures through an infrared sensor, and performing image recognition to obtain gesture control instruction data; generating interaction control data according to the three-dimensional coordinates in the real image data, the voice control instruction and the gesture control instruction; aiming at low image imaging precision in a vehicle-mounted VR entertainment system in the prior art, high-quality aerial imaging is generated by using a DCT plate negative reflection plate lens and a plane mirror, multi-modal interaction and the like are realized by adopting voice recognition and gesture recognition, and a vivid imaging effect is provided.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing text-to-speech modeling. In some implementations, a computer device receives input text in a first language to convert to a desired speech in a second language. The computer device receives one or more criteria for modifying the desired speech and converts the input text to a desired text in the second language. The computer device generates audio representations of the desired text in the second language and predicts, for each of the audio representations of the desired text and using the one or more received criteria, a pitch value and a duration value. The computer device generates the desired output speech using the predicted pitch value and the predicted duration value for each of the audio representations and provides the desired speech for output.

The invention relates to the technical field of old-age care robots, in particular to a medical monitoring type old-age care robot which comprises a physiological monitoring unit, an air particle regulation and health feedback unit, an emotion and health fusion unit and a nursing auxiliary unit. The physiological monitoring unit is used for collecting physiological data of the old people in real time; the air particle regulation and control unit is used for detecting pollutants and biological particles in the air, analyzing the air quality in real time and automatically regulating air purification equipment or an external ventilation device in the robot body according to the particle concentration in the air and physiological data of the old people; the emotion and health fusion unit analyzes the mental health of the elderly; the nursing auxiliary unit comprises a mechanical arm and a voice control system and assists the old in daily activities. According to the invention, psychological support and emotional care are provided for the elderly in time, the life quality of the elderly is improved, and psychological health problems are effectively prevented.

The invention relates to the technical field of vehicles, in particular to a vehicle voice control method and device, a vehicle and a storage medium, and the method comprises the steps: collecting voice information of at least one driver and passenger in a vehicle cabin; performing message text rewriting on the voice information by using the user vehicle control intention recognition model to obtain a rewritten message text; based on an information retrieval enhancement technology, determining a target similarity text of the rewritten message text, inputting the rewritten message text and the target similarity text into a user vehicle control intention recognition model to recognize a voice control intention of a driver and a passenger, and generating a voice control instruction of a vehicle according to the voice control intention, and executing a control action corresponding to the voice control instruction. Therefore, the problems that in the related technology, due to the fact that a large number of rules and a large number of mapping functions are written, voice recognition is limited, due to too many rules, voice recognition is disordered, and the accuracy and intelligence of vehicle voice control are reduced are solved.

Systems and methods for imaging, such as with an intravascular ultrasound (IVUS) imaging device are employed with components and accessories, such as for example, catheters, a catheter interface module, mounts, a workstation, a tablet, a display, and cart. The components may be controlled or interacted with via hands-free commands, voice control, gestures, and may be enhanced with artificial intelligence and / or machine learning. Real time measurements and border detection with enhanced imaging are provided.

The invention discloses a non-contact operating room intelligent voice control method supporting multi-role voiceprint recognition, and relates to the technical field of voice interaction control, and the method comprises the following steps: S100, collecting full-band noise signals of all high-frequency operation equipment in an operating room in different operation states, extracting harmonic frequency, amplitude and phase features, and carrying out the recognition of the full-band noise signals; and constructing a harmonic noise characteristic database, and establishing and dynamically updating an environmental noise baseline model. According to the invention, through the dynamic environment noise baseline model and personalized voiceprint recognition, frequency spectrum isolation and accurate voice separation of high-frequency operation equipment noise and doctor voiceprint are realized. In combination with space and frequency spectrum dual-domain decoupling and voice integrity detection, and through dual verification of voiceprint identity and non-contact action, it is ensured that an instruction sender is true and effective. And a closed-loop defense mechanism is formed through permission level comparison and context semantic verification, so that error identification and error control are effectively prevented, and the safety, accuracy and continuity of the operation process are guaranteed.

The invention provides an embedded voice recognition control module and an equipment interaction system thereof. The embedded voice recognition control module comprises a voice acquisition sub-module, a voice preprocessing sub-module, a voice recognition and command analysis sub-module, a control command generation sub-module, an actuator and feedback sub-module and a self-adaptive learning and personalization sub-module; a deep learning speech recognition technology and a natural language processing technology are integrated in the small embedded speech recognition control module, so that speech control instruction recognition and feedback can be completed with low power consumption, dependence of speech control on a network is reduced, the response speed is increased, and the speech control efficiency is improved. And moreover, the privacy of the user can be better protected, and the deep learning speech recognition model can be adaptively trained based on the character use habit of the user and the accent of the user, so that the speech recognition accuracy is continuously improved.

The invention relates to the technical field of bionic humanoid robots, in particular to a multi-expression driving bionic intelligent robot head which comprises an eyebrow movable layer, a cheekbone movable layer, an eye movable layer, an upper lip movable layer and a chin movable layer located below the upper lip movable layer, and the eyebrow movable layer, the cheekbone movable layer, the eye movable layer and the upper lip movable layer are spliced and assembled by hexagonal threaded copper columns through reserved hole positions. The robot head is composed of a shell, an aluminum alloy machining plate frame, a CNC metal part, a steering engine, a rail and a pull wire, and a camera module and a voice control module are arranged in the robot head. The shell can be customized according to different figure facial features, the shell is of a high-precision, compact and adjustable structure, 26 steering engines are integrated in a limited space to control opening and closing of the face including left and right eyeballs, upper and lower eyelids, canthus, eyebrows, cheeks, mouth corners and mouth and actions of lips, and the layered design and control mode of the head of the robot enable the robot to be more intelligent. And the mounting, the dismounting and the maintenance are more convenient.

The invention discloses a display equipment intelligent voice interaction method based on a large language model, and the method comprises the steps: constructing a set of display equipment intelligent voice interaction system suitable for a complex scene based on a large language model technical framework through integrating a multi-stage audio processing module, an intelligent voice recognition module, a semantic understanding and intention recognition module and a multi-mode interaction control module; the voice enhancement technology is adopted, high-precision voice instruction capture and noise reduction processing can be achieved in a noisy environment, and geographic space parameters and operation logic implied in user intentions are accurately analyzed in combination with a deep semantic analysis engine; through a multi-mode fusion control mechanism, real-time linkage interaction between a voice instruction and display equipment is supported, natural and smooth voice control experience is provided especially for scenes such as three-dimensional space positioning and topographic data calling in a holographic geographical environment, and the system has strong error recovery capability and good expansibility and is suitable for being popularized and applied. The method is especially suitable for a voice control interaction scene in a holographic environment.

The invention provides a multi-scene illumination management system integrating voice control and remote monitoring, and relates to the technical field of illumination management, and the system comprises the steps: receiving a scene wake-up instruction collected by a microphone array and a scene switching instruction sent by a remote monitoring terminal; adding priority labels and timestamps to the scene wakeup instruction and the scene switching instruction, and when it is detected that a timestamp difference value between the scene wakeup instruction and the scene switching instruction is smaller than a conflict time threshold value, determining an instruction arbitration coefficient through preemptive priority weights of the scene wakeup instruction and the scene switching instruction, determining an effective instruction according to the instruction arbitration coefficient and all the priority labels; and when the instruction priority of the effective instruction is greater than the instruction priority of the currently executed illumination scene instruction, generating a coverage execution signal, otherwise, generating an instruction discarding signal, further outputting an illumination control signal, and adjusting light parameters of the illumination equipment through the illumination control signal. According to the invention, multi-control-source conflicts in a lighting management system can be eliminated.

The invention relates to the technical field of voice recognition, in particular to a Bluetooth headset AI voice control method and system, and the method comprises the following steps: collecting a voice sample, carrying out the feature extraction of a voice signal through employing a Mel-frequency cepstrum coefficient, and generating voice feature data; and inputting the voice feature data into an acoustic model, and improving the recognition rate of the key instruction words by the acoustic model through learning features to obtain an optimized acoustic model. According to the method, acoustic feature capture is realized through voice signal feature extraction, the recognition accuracy of the key instruction is improved in cooperation with deep learning of acoustic features, the influence of different user speech speeds on the recognition effect is overcome by applying the time alignment technology of voice input, and the stability of the control instruction in different use scenes and speech speed changes is ensured. In addition, the recognition capability of a specific command is further mined and optimized by means of statistical characteristic analysis of voice, and efficient and automatic recognition and extraction of key control commands in continuous speech streams are completed.

Various implementations include approaches for voice control in audio devices. In some cases, a method includes: listening, using at least one audio capture device, for user input to control at least one attribute of an audio device; routing the user input through a machine learning (ML) model to determine a control action for the at least one attribute based on the user input; and causing the determined control action to be performed, wherein the ML model need not have been pre-trained with the user input to determine the control action for the at least one attribute of the audio device.

The invention relates to the technical field of smart home, and discloses an equipment control method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring an original control instruction set; performing multi-language expression conversion on each original control instruction to generate various variant control instructions matched with the original control instructions, and executing the same target operation on the variant control instructions and the original control instructions; for any variant control instruction, role conversion is carried out on the variant control instruction, and a voice control instruction set corresponding to each role is generated; and training the target control model by using the voice control instruction set, so that the target control model responds to the voice control instruction set and controls the target equipment to execute the target operation. By implementing the technical scheme of the invention, the adaptability of the smart home device to different user language habits and role identities is improved, and the naturalness and flexibility of voice interaction are enhanced.

Systems and methods are described herein for enabling, on a local device, a voice control system that limits the amount of data needed to be transmitted to a remote server. A data structure is built at the local device to support a local speech-to-text model by receiving a query and transmitting, to a remote server over a communication network, a request for a speech-to-text transcription of the query. The transcription is received from the remote server and stored in the data structure at the local device in association with an audio clip of the query. Metadata describing the query is used to train the local speech-to-text model to recognize future instances of the query.

The invention discloses a large model tool calling method and system based on risk assessment, and the method comprises the following steps: receiving a natural language instruction of a user, and obtaining a calling request of the user based on a large model; generating a risk score based on the user call request; generating a confirmation strategy based on the risk score, the user preference adjustment factor and the emergency degree adjustment factor; executing a corresponding confirmation process based on the confirmation strategy; based on the confirmation process, the controller executes tool calling; by the adoption of the technical scheme, the problem of balance between safety and user experience in tool calling is solved, a safer and more intelligent interaction mechanism is provided for voice control and large language model driven tool calling, a risk self-adaptive confirmation process and a multi-stage confirmation mechanism provide sufficient safety guarantee, and meanwhile the safety of a user is improved. And the user interaction experience is improved to the greatest extent.

The invention relates to a voice control method, device and system, a household appliance, a storage medium and a product, and the method comprises the steps: firstly obtaining an environment noise parameter of a current environment under the condition of detecting that a user sends a voice wake-up word, and then carrying out the adaptation of voice recognition sensitivity and voice reply volume for the current environment through combining with the environment noise parameter. And finally, related actions of voice control are executed according to the adaptive voice recognition sensitivity and the voice reply volume, namely, a voice action instruction is recognized, and a reply signal is fed back to the user based on the voice action instruction. Therefore, the voice action instruction sent by the user can be accurately recognized, the reply signal can be transmitted to the user with enough volume, and the possibility of voice control failure is effectively reduced.

The invention relates to the technical field of intelligent management, in particular to an intelligent medicine safety management system based on voice navigation and an application method of the intelligent medicine safety management system. The storage unit is arranged to be used for storing a plurality of medical articles, and the medical articles are provided with attribute information including the valid period; the triggering module is used for opening the storage unit and triggering and reminding the position of the matched medical article or triggering the medical article to be discharged to the medicine outlet; the identity recognition module is used for recognizing personnel information of personnel operating the mobile medicine vehicle; and the voice control module is configured to receive voice information of a person at first, then identify key information including the medical articles from the voice information, then find the positions of the corresponding medical articles from the storage unit, and then transmit the position information to the trigger module to obtain the corresponding medical articles. The defects of an intelligent management system in the aspects of traceability and accurate management are overcome.

The embodiment of the invention is applied to the technical field of voice control, and provides a voice control method, a device selection method and an electronic device. Under the condition that the first electronic equipment receives a voice instruction sent by a user, if the voice instruction comprises an awakening word corresponding to target equipment, the first electronic equipment sends an action instruction to the target equipment according to the voice instruction; wherein the action instruction is used for representing the action, which the user wants to execute by the target equipment, in the voice instruction. In the application, remote interaction between the electronic devices can be realized, the accuracy of action execution of the target device is improved, and then the interaction experience of a user is improved.