Artificial intelligence apparatus and method for recognizing utterance voice of user

Embodiments provide an artificial intelligence apparatus for recognizing an utterance voice of a user. The artificial intelligence apparatus includes: a communication unit configured to communicate with at least one external artificial intelligence apparatus which obtains first sound data including the utterance voice of the user to generate a first speech recognition result from the first sound data; a microphone configured to obtain second sound data including the utterance voice; and a processor configured to receive first speech recognition results from each of the at least one external artificial intelligence apparatus, generate a second speech recognition result from the second sound data, generate a final speech recognition result for the utterance voice by using the first speech recognition results and the second speech recognition result, and perform a control corresponding to the final speech recognition result.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. 119 and 35 U.S.C. 365 to Korean Patent Application No. 10-2019-0102695 filed on Aug. 21, 2019, which is hereby incorporated by reference in its entirety.

BACKGROUND

The present disclosure relates to an artificial intelligence apparatus and a method for recognizing an utterance voice of a user. More particularly, the present disclosure relates to an artificial intelligence apparatus and a method for recognizing an utterance voice of a user by attempting to recognize the utterance voice of the user through a plurality of artificial intelligence apparatuses, and collecting speech recognition results from each of the artificial intelligence apparatuses.

Recently, there is a trend that services that employ a speech recognition technology such as artificial intelligence speakers, voice control, and voice secretary. In addition, users having a plurality of artificial intelligence apparatuses in one space are increasing.

However, in the related art, even if a user speaks in a space provided with the artificial intelligence apparatuses, each of the artificial intelligence apparatuses individually attempts speech recognition.

If speech recognition results of the artificial intelligence apparatuses are properly collected, an utterance voice of the user may be recognized with higher accuracy.

SUMMARY

Embodiments provide an artificial intelligence apparatus and a method thereof, in which weights between artificial intelligence apparatuses are determined in consideration of an environment at an utterance time of a user, and speech recognition results for an utterance voice of the user that are obtained from each of the artificial intelligence apparatuses are collected based on the determined weights so as to generate a final speech recognition result.

In one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which a speech recognition result generated from voice data corresponding to an utterance voice of a user is received from at least one artificial intelligence apparatus, a speech recognition result is directly generated from the voice data corresponding to the utterance voice of the user, a final speech recognition result is generated by using the received speech recognition result and the generated speech recognition result, and a control corresponding to the generated final speech recognition result is performed.

In addition, in one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which an environment variable corresponding to an utterance time is determined based on the received voice data, a weight for each of the artificial intelligence apparatuses is determined based on the determined environment variable, and the final speech recognition result is generated from the received speech recognition result and the generated speech recognition result based on the determined weight.

In addition, in one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which a noise type included in sound data is determined, and the weight for each of the artificial intelligence apparatuses is determined based on the determined noise type.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes “module” and “unit” for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included.

It will be understood that the terms “first” and “second” are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components.

In this disclosure below, when one part (or element, device, etc.) is referred to as being ‘connected’ to another part (or element, device, etc.), it should be understood that the former can be ‘directly connected’ to the latter, or ‘electrically connected’ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being ‘directly connected’ or ‘directly linked’ to another component, it means that no intervening component is present.

Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.

An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.

The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.

The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network.

Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.

The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the training data is input to the artificial neural network. The unsupervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is not given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state.

Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep learning is part of machine learning. In the following, machine learning is used to mean deep learning.

A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot.

Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.

The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air.

Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user.

For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set.

The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like.

Here, the self-driving vehicle may be regarded as a robot having a self-driving function.

Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world.

The MR technology is similar to the AR technology in that the real object and the virtual object are shown together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner.

The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device.

FIG. 1is a block diagram illustrating an AI apparatus100according to an embodiment of the present invention.

Hereinafter, the AI apparatus100may be referred to as a terminal.

Referring toFIG. 1, the AI apparatus100may include a communication unit110, an input unit120, a learning processor130, a sensing unit140, an output unit150, a memory170, and a processor180.

The communication unit110may transmit and receive data to and from external devices such as other AI apparatuses100ato100eand the AI server200by using wire/wireless communication technology. For example, the communication unit110may transmit and receive sensor information, a user input, a learning model, and a control signal to and from external devices.

The communication technology used by the communication unit110includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.

The input unit120may acquire various kinds of data.

Here, the input unit120may include a camera for inputting a video signal, a microphone for receiving an audio signal, and a user input unit for receiving information from a user. The camera or the microphone may be treated as a sensor, and the signal acquired from the camera or the microphone may be referred to as sensing data or sensor information.

The input unit120may acquire training data for model learning and input data to be used when an output is acquired by using learning model. The input unit120may acquire raw input data. Here, the processor180or the learning processor130may extract an input feature by preprocessing the input data.

The learning processor130may learn a model composed of an artificial neural network by using training data. The learned artificial neural network may be referred to as a learning model. The learning model may be used to an infer result value for new input data rather than training data, and the inferred value may be used as a basis for determination to perform a certain operation.

Here, the learning processor130may perform AI processing together with the learning processor240of the AI server200.

Here, the learning processor130may include a memory integrated or implemented in the AI apparatus100. Alternatively, the learning processor130may be implemented by using the memory170, an external memory directly connected to the AI apparatus100, or a memory held in an external device.

The sensing unit140may acquire at least one of internal information about the AI apparatus100, ambient environment information about the AI apparatus100, and user information by using various sensors.

Examples of the sensors included in the sensing unit140may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, and a radar.

The output unit150may generate an output related to a visual sense, an auditory sense, or a haptic sense.

Here, the output unit150may include a display unit for outputting time information, a speaker for outputting auditory information, and a haptic module for outputting haptic information.

The memory170may store data that supports various functions of the AI apparatus100. For example, the memory170may store input data acquired by the input unit120, training data, a learning model, a learning history, and the like.

The processor180may determine at least one executable operation of the AI apparatus100based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor180may control the components of the AI apparatus100to execute the determined operation.

To this end, the processor180may request, search, receive, or utilize data of the learning processor130or the memory170. The processor180may control the components of the AI apparatus100to execute the predicted operation or the operation determined to be desirable among the at least one executable operation.

When the connection of an external device is required to perform the determined operation, the processor180may generate a control signal for controlling the external device and may transmit the generated control signal to the external device.

The processor180may acquire intention information for the user input and may determine the user's requirements based on the acquired intention information.

The processor180may acquire the intention information corresponding to the user input by using at least one of a speech to text (STT) engine for converting speech input into a text string or a natural language processing (NLP) engine for acquiring intention information of a natural language.

At least one of the STT engine or the NLP engine may be configured as an artificial neural network, at least part of which is learned according to the machine learning algorithm. At least one of the STT engine or the NLP engine may be learned by the learning processor130, may be learned by the learning processor240of the AI server200, or may be learned by their distributed processing.

The processor180may collect history information including the operation contents of the AI apparatus100or the user's feedback on the operation and may store the collected history information in the memory170or the learning processor130or transmit the collected history information to the external device such as the AI server200. The collected history information may be used to update the learning model.

The processor180may control at least part of the components of AI apparatus100so as to drive an application program stored in memory170. Furthermore, the processor180may operate two or more of the components included in the AI apparatus100in combination so as to drive the application program.

FIG. 2is a block diagram illustrating an AI server200according to an embodiment of the present invention.

Referring toFIG. 2, the AI server200may refer to a device that learns an artificial neural network by using a machine learning algorithm or uses a learned artificial neural network. The AI server200may include a plurality of servers to perform distributed processing, or may be defined as a 5G network. Here, the AI server200may be included as a partial configuration of the AI apparatus100, and may perform at least part of the AI processing together.

The AI server200may include a communication unit210, a memory230, a learning processor240, a processor260, and the like.

The communication unit210can transmit and receive data to and from an external device such as the AI apparatus100.

The memory230may include a model storage unit231. The model storage unit231may store a learning or learned model (or an artificial neural network231a) through the learning processor240.

The learning processor240may learn the artificial neural network231aby using the training data. The learning model may be used in a state of being mounted on the AI server200of the artificial neural network, or may be used in a state of being mounted on an external device such as the AI apparatus100.

The learning model may be implemented in hardware, software, or a combination of hardware and software. If all or part of the learning models are implemented in software, one or more instructions that constitute the learning model may be stored in memory230.

The processor260may infer the result value for new input data by using the learning model and may generate a response or a control command based on the inferred result value.

FIG. 3is a view illustrating an AI system1according to an embodiment of the present invention.

Referring toFIG. 3, in the AI system1, at least one of an AI server200, a robot100a, a self-driving vehicle100b, an XR device100c, a smartphone100d, or a home appliance100eis connected to a cloud network10. The robot100a, the self-driving vehicle100b, the XR device100c, the smartphone100d, or the home appliance100e, to which the AI technology is applied, may be referred to as AI apparatuses100ato100e.

The cloud network10may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure. The cloud network10may be configured by using a 3G network, a 4G or LTE network, or a 5G network.

That is, the devices100ato100eand200configuring the AI system1may be connected to each other through the cloud network10. In particular, each of the devices100ato100eand200may communicate with each other through a base station, but may directly communicate with each other without using a base station.

The AI server200may include a server that performs AI processing and a server that performs operations on big data.

The AI server200may be connected to at least one of the AI apparatuses constituting the AI system1, that is, the robot100a, the self-driving vehicle100b, the XR device100e, the smartphone100d, or the home appliance100ethrough the cloud network10, and may assist at least part of AI processing of the connected AI apparatuses100ato100e.

Here, the AI server200may learn the artificial neural network according to the machine learning algorithm instead of the AI apparatuses100ato100e, and may directly store the learning model or transmit the learning model to the AI apparatuses100ato100e.

Here, the AI server200may receive input data from the AI apparatuses100ato100e, may infer the result value for the received input data by using the learning model, may generate a response or a control command based on the inferred result value, and may transmit the response or the control command to the AI apparatuses100ato100e.

Alternatively, the AI apparatuses100ato100emay infer the result value for the input data by directly using the learning model, and may generate the response or the control command based on the inference result.

Hereinafter, various embodiments of the AI apparatuses100ato100eto which the above-described technology is applied will be described. The AI apparatuses100ato100eillustrated inFIG. 3may be regarded as a specific embodiment of the AI apparatus100illustrated inFIG. 1.

The robot100a, to which the AI technology is applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like.

The robot100amay include a robot control module for controlling the operation, and the robot control module may refer to a software module or a chip implementing the software module by hardware.

The robot100amay acquire state information about the robot100aby using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, may determine the response to user interaction, or may determine the operation.

The robot100amay use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan.

The robot100amay perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the robot100amay recognize the surrounding environment and the objects by using the learning model, and may determine the operation by using the recognized surrounding information or object information. The learning model may be learned directly from the robot100aor may be learned from an external device such as the AI server200.

Here, the robot100amay perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server200and the generated result may be received to perform the operation.

The robot100amay use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the driving unit such that the robot100atravels along the determined travel route and travel plan.

The map data may include object identification information about various objects arranged in the space in which the robot100amoves. For example, the map data may include object identification information about fixed objects such as walls and doors and movable objects such as pollen and desks. The object identification information may include a name, a type, a distance, and a position.

In addition, the robot100amay perform the operation or travel by controlling the driving unit based on the control/interaction of the user. Here, the robot100amay acquire the intention information of the interaction due to the user's operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation.

The self-driving vehicle100b, to which the AI technology is applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like.

The self-driving vehicle100bmay include a self-driving control module for controlling a self-driving function, and the self-driving control module may refer to a software module or a chip implementing the software module by hardware. The self-driving control module may be included in the self-driving vehicle100bas a component thereof, but may be implemented with separate hardware and connected to the outside of the self-driving vehicle100b.

The self-driving vehicle100bmay acquire state information about the self-driving vehicle100bby using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, or may determine the operation.

Like the robot100a, the self-driving vehicle100bmay use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan.

In particular, the self-driving vehicle100bmay recognize the environment or objects for an area covered by a field of view or an area over a certain distance by receiving the sensor information from external devices, or may receive directly recognized information from the external devices.

The self-driving vehicle100bmay perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the self-driving vehicle100bmay recognize the surrounding environment and the objects by using the learning model, and may determine the traveling route by using the recognized surrounding information or object information. The learning model may be learned directly from the self-driving vehicle100aor may be learned from an external device such as the AI server200.

Here, the self-driving vehicle100bmay perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server200and the generated result may be received to perform the operation.

The self-driving vehicle100bmay use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the driving unit such that the self-driving vehicle100btravels along the determined travel route and travel plan.

The map data may include object identification information about various objects arranged in the space (for example, road) in which the self-driving vehicle100btravels. For example, the map data may include object identification information about fixed objects such as street lamps, rocks, and buildings and movable objects such as vehicles and pedestrians. The object identification information may include a name, a type, a distance, and a position.

In addition, the self-driving vehicle100bmay perform the operation or travel by controlling the driving unit based on the control/interaction of the user. Here, the self-driving vehicle100bmay acquire the intention information of the interaction due to the user's operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation.

The XR device100c, to which the AI technology is applied, may be implemented by a head-mount display (HMD), a head-up display (HUD) provided in the vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a fixed robot, a mobile robot, or the like.

The XR device100cmay analyzes three-dimensional point cloud data or image data acquired from various sensors or the external devices, generate position data and attribute data for the three-dimensional points, acquire information about the surrounding space or the real object, and render to output the XR object to be output. For example, the XR device100cmay output an XR object including the additional information about the recognized object in correspondence to the recognized object.

The XR device100cmay perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the XR device100cmay recognize the real object from the three-dimensional point cloud data or the image data by using the learning model, and may provide information corresponding to the recognized real object. The learning model may be directly learned from the XR device100c, or may be learned from the external device such as the AI server200.

Here, the XR device100cmay perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server200and the generated result may be received to perform the operation.

The robot100a, to which the AI technology and the self-driving technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like.

The robot100a, to which the AI technology and the self-driving technology are applied, may refer to the robot itself having the self-driving function or the robot100ainteracting with the self-driving vehicle100b.

The robot100ahaving the self-driving function may collectively refer to a device that moves for itself along the given route without the user's control or moves for itself by determining the route by itself.

The robot100aand the self-driving vehicle100bhaving the self-driving function may use a common sensing method so as to determine at least one of the travel route or the travel plan. For example, the robot100aand the self-driving vehicle100bhaving the self-driving function may determine at least one of the travel route or the travel plan by using the information sensed through the lidar, the radar, and the camera.

The robot100athat interacts with the self-driving vehicle100bexists separately from the self-driving vehicle100band may perform operations interworking with the self-driving function of the self-driving vehicle100bor interworking with the user who rides on the self-driving vehicle100b.

Here, the robot100ainteracting with the self-driving vehicle100bmay control or assist the self-driving function of the self-driving vehicle100bby acquiring sensor information on behalf of the self-driving vehicle100band providing the sensor information to the self-driving vehicle100b, or by acquiring sensor information, generating environment information or object information, and providing the information to the self-driving vehicle100b.

Alternatively, the robot100ainteracting with the self-driving vehicle100bmay monitor the user boarding the self-driving vehicle100b, or may control the function of the self-driving vehicle100bthrough the interaction with the user. For example, when it is determined that the driver is in a drowsy state, the robot100amay activate the self-driving function of the self-driving vehicle100bor assist the control of the driving unit of the self-driving vehicle100b. The function of the self-driving vehicle100bcontrolled by the robot100amay include not only the self-driving function but also the function provided by the navigation system or the audio system provided in the self-driving vehicle100b.

Alternatively, the robot100athat interacts with the self-driving vehicle100bmay provide information or assist the function to the self-driving vehicle100boutside the self-driving vehicle100b. For example, the robot100amay provide traffic information including signal information and the like, such as a smart signal, to the self-driving vehicle100b, and automatically connect an electric charger to a charging port by interacting with the self-driving vehicle100blike an automatic electric charger of an electric vehicle.

The robot100a, to which the AI technology and the XR technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, or the like.

The robot100a, to which the XR technology is applied, may refer to a robot that is subjected to control/interaction in an XR image. In this case, the robot100amay be separated from the XR device100cand interwork with each other.

When the robot100a, which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the robot100aor the XR device100cmay generate the XR image based on the sensor information, and the XR device100cmay output the generated XR image. The robot100amay operate based on the control signal input through the XR device100cor the user's interaction.

For example, the user can confirm the XR image corresponding to the time point of the robot100ainterworking remotely through the external device such as the XR device100c, adjust the self-driving travel path of the robot100athrough interaction, control the operation or driving, or confirm the information about the surrounding object.

The self-driving vehicle100b, to which the AI technology and the XR technology are applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like.

The self-driving driving vehicle100b, to which the XR technology is applied, may refer to a self-driving vehicle having a means for providing an XR image or a self-driving vehicle that is subjected to control/interaction in an XR image. Particularly, the self-driving vehicle100bthat is subjected to control/interaction in the XR image may be distinguished from the XR device100cand interwork with each other.

The self-driving vehicle100bhaving the means for providing the XR image may acquire the sensor information from the sensors including the camera and output the generated XR image based on the acquired sensor information. For example, the self-driving vehicle100bmay include an HUD to output an XR image, thereby providing a passenger with a real object or an XR object corresponding to an object in the screen.

Here, when the XR object is output to the HUD, at least part of the XR object may be outputted so as to overlap the actual object to which the passenger's gaze is directed. Meanwhile, when the XR object is output to the display provided in the self-driving vehicle100b, at least part of the XR object may be output so as to overlap the object in the screen. For example, the self-driving vehicle100bmay output XR objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, and the like.

When the self-driving vehicle100b, which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the self-driving vehicle100bor the XR device100cmay generate the XR image based on the sensor information, and the XR device100cmay output the generated XR image. The self-driving vehicle100bmay operate based on the control signal input through the external device such as the XR device100cor the user's interaction.

FIG. 4is a block diagram illustrating an AI apparatus100according to an embodiment of the present invention.

The redundant repeat ofFIG. 1will be omitted below.

Referring toFIG. 4, the input unit120may include a camera121for image signal input, a microphone122for receiving audio signal input, and a user input unit123for receiving information from a user.

Voice data or image data collected by the input unit120are analyzed and processed as a user's control command.

Then, the input unit120is used for inputting image information (or signal), audio information (or signal), data, or information inputted from a user and the AI apparatus100may include at least one camera121in order for inputting image information.

The camera121processes image frames such as a still image or a video obtained by an image sensor in a video call mode or a capturing mode. The processed image frame may be displayed on the display unit151or stored in the memory170.

The microphone122processes external sound signals as electrical voice data. The processed voice data may be utilized variously according to a function (or an application program being executed) being performed in the AI apparatus100. Moreover, various noise canceling algorithms for removing noise occurring during the reception of external sound signals may be implemented in the microphone122.

The user input unit123is to receive information from a user and when information is inputted through the user input unit123, the processor180may control an operation of the AI apparatus100to correspond to the inputted information.

The user input unit123may include a mechanical input means (or a mechanical key, for example, a button, a dome switch, a jog wheel, and a jog switch at the front, back or side of the AI apparatus100) and a touch type input means. As one example, a touch type input means may include a virtual key, a soft key, or a visual key, which is displayed on a touch screen through software processing or may include a touch key disposed at a portion other than the touch screen.

The sensing unit140may also be referred to as a sensor unit.

The output unit150may include at least one of a display unit151, a sound output module152, a haptic module153, or an optical output module154.

The display unit151may be formed with a mutual layer structure with a touch sensor or formed integrally, so that a touch screen may be implemented. Such a touch screen may serve as the user input unit123providing an input interface between the AI apparatus100and a user, and an output interface between the AI apparatus100and a user at the same time.

The sound output module152may output audio data received from the wireless communication unit110or stored in the memory170in a call signal reception or call mode, a recording mode, a voice recognition mode, or a broadcast reception mode.

The sound output module152may include a receiver, a speaker, and a buzzer.

The haptic module153generates various haptic effects that a user can feel. A representative example of a haptic effect that the haptic module153generates is vibration.

The optical output module154outputs a signal for notifying event occurrence by using light of a light source of the AI apparatus100. An example of an event occurring in the AI apparatus100includes message reception, call signal reception, missed calls, alarm, schedule notification, e-mail reception, and information reception through an application.

FIG. 5is a view illustrating an AI system1according to an embodiment of the present invention.

Referring toFIG. 5, an artificial intelligence system1may include a plurality of artificial intelligence apparatuses100and an artificial intelligence server200.

At least some of the artificial intelligence apparatuses100or the artificial intelligence server200may communicate with each other using a wired or wireless communication technology.

Here, each of apparatuses100and200may communicate with each other through a base station, a router, or the like, and may directly communicate with each other using a short range communication technology.

For example, each of the apparatuses100and200may communicate with each other directly or via the base station by using 5thgeneration (5G) communication.

InFIG. 5, the artificial intelligence server200is shown as being included in the artificial intelligence system1, but the present invention is not limited thereto. In other words, the artificial intelligence system1may not include the artificial intelligence server200, but may include only a plurality of artificial intelligence apparatuses100.

In one embodiment, the artificial intelligence server200may receive data required for recognizing a voice of a user from the artificial intelligence apparatuses100, and may recognize an utterance voice of the user based on the received data.

In one embodiment, the artificial intelligence apparatus100may receive the data required for recognizing the voice of the user from other artificial intelligence apparatuses100, and may recognize the utterance voice of the user based on data obtained by the artificial intelligence apparatus100and the received data. In this case, the artificial intelligence apparatus100that receives the data required for recognizing the voice of the user from other artificial intelligence apparatuses100to recognize the utterance voice of the user may operate as a master artificial intelligence apparatus in relation to other artificial intelligence apparatuses100.

In the following, the artificial intelligence apparatus100or the artificial intelligence server200configured to control other artificial intelligence apparatuses100and recognize the voice of the user by using other artificial intelligence apparatuses100may be referred to as a master artificial intelligence apparatus. In other words, the master artificial intelligence apparatus may be the artificial intelligence apparatus100or may be the artificial intelligence server200. In addition, the artificial intelligence apparatuses100controlled by the master artificial intelligence apparatus may be referred to as slave artificial intelligence apparatuses.

The master artificial intelligence apparatus may be configured to communicate with the slave artificial intelligence apparatuses, and may control the slave artificial intelligence apparatuses. Accordingly, the master artificial intelligence apparatus may organically control a plurality of artificial intelligence apparatuses100by controlling the slave artificial intelligence apparatuses.

The data required for recognizing the utterance voice of the user may include sound data corresponding to the utterance voice of the user, a speech recognition result obtained from the sound data by using a speech recognition model, and the like.

FIG. 6is a view illustrating an AI system1according to an embodiment of the present invention.

Referring toFIG. 6, the artificial intelligence system according to one embodiment may include a plurality of artificial intelligence apparatuses611to615. Although not shown inFIG. 6, the artificial intelligence system1may include the artificial intelligence server200.

For example, the artificial intelligence system1may include a washing machine611, a refrigerator612, a speaker613, a TV614, an air conditioner615, and the like as the artificial intelligence apparatuses100.

Each of the artificial intelligence apparatuses611to615may be provided with a microphone122to obtain sound data of a user601.

As shown inFIG. 6, the user601may utter a control command or a query to perform a specific operation by using the artificial intelligence system1. In addition, the utterance voice of the user601may be received through the artificial intelligence apparatuses611to615.

The utterance voice of the user601received by each of the artificial intelligence apparatuses611to615may vary depending on a distance to the user601or a spatial structure. In addition, speech recognition models (or speech recognition engines) used by the artificial intelligence apparatuses611to615may be different from each other, so that speech recognition accuracies of the artificial intelligence apparatuses611to615may be different from each other.

The artificial intelligence apparatus100or the artificial intelligence server200functioning as the master artificial intelligence apparatus in the artificial intelligence system1may recognize the voice of the user601more accurately by using speech recognition results of other artificial intelligence apparatuses100.

In an example ofFIG. 6, the master artificial intelligence apparatus may be one of the washing machine611, the refrigerator612, the speaker613, the TV614, or the air conditioner615, or may be the artificial intelligence server200which is not shown inFIG. 6.

For example, when the user601performs an utterance602in a kitchen, such as “Turn off the kitchen light.”, the utterance voice of the user601may be received by the washing machine611, the refrigerator612, the speaker613, the TV614, and the air conditioner615. The master artificial intelligence apparatus may recognize the utterance voice of the user601by using speech recognition results of the artificial intelligence apparatuses611to615, and perform necessary operations based on the recognition results.

Each of the artificial intelligence apparatuses611to615stores a speech recognition model, and may attempt to recognize the utterance voice of the user601by using the stored speech recognition model.

In addition, each of the artificial intelligence apparatuses611to615may store a timestamp that indicates a reception time of the utterance voice. In order to ensure validity of the timestamp, time information of the master artificial intelligence apparatus may be synchronized with time information of the artificial intelligence apparatuses611to615.

The timestamp indicating the reception time of the voice may be used to determine a reception delay time or a reception time difference between the artificial intelligence apparatuses611to615, and the master artificial intelligence apparatus may determine positional relation between each of the artificial intelligence apparatuses611to615and the user601based on the reception delay time of each of the artificial intelligence apparatuses611to615. To this end, the master artificial intelligence apparatus may store positional relation between the artificial intelligence apparatuses611to615.

For example, the master artificial intelligence apparatus may determine a direction of the user601, which is a sound source, from each of the artificial intelligence apparatuses611to615by using a direction-of-arrival (DOA) algorithm. In addition, the positional relation between each of the artificial intelligence apparatuses611to615and the user601may be determined by collecting directional information representing a location of the user601from each of the artificial intelligence apparatuses611to615.

FIG. 7is a flowchart illustrating a method of recognizing a voice of a user according to one embodiment.

In detail,FIG. 7illustrates an embodiment in which one artificial intelligence apparatus100among the artificial intelligence apparatuses constituting the artificial intelligence system1operates as the master artificial intelligence apparatus to recognize the utterance voice of the user.

Referring toFIG. 7, the processor180of the artificial intelligence apparatus100receives a speech recognition result for the utterance voice of the user from each of at least one external artificial intelligence apparatus through the communication unit110(S701).

The external artificial intelligence apparatus is an apparatus having a microphone to perform a function of recognizing an utterance voice of a user, and refers to a slave artificial intelligence apparatus. Meanwhile, the artificial intelligence apparatus100that receives the speech recognition result from at least one external artificial intelligence apparatus refers to a master artificial intelligence apparatus.

The external artificial intelligence apparatus may obtain the sound data including the utterance voice of the user, and generate the speech recognition result from the obtained sound data. In addition, the artificial intelligence apparatus100may receive the speech recognition results generated from the external artificial intelligence apparatuses. The sound data obtained by each of the external artificial intelligence apparatuses includes the same utterance voice, and the obtained sound data may vary depending on an ambient noise, the spatial structure, the positional relation with the user, and the like.

The obtaining of the sound data may refer to converting a sound wave corresponding to the utterance voice of the user into a sound signal or sound data through a microphone. The generating of the speech recognition result from the sound data may refer to converting the utterance voice into a text.

The external artificial intelligence apparatus may generate the speech recognition result from the sound data by using the speech recognition model individually provided in each external artificial intelligence apparatus. For example, when the artificial intelligence system includes two external artificial intelligence apparatuses, a first external artificial intelligence apparatus may generate a first speech recognition result by using a first speech recognition model, and a second external artificial intelligence apparatus may generate a second speech recognition result by using a second speech recognition model. In addition, the processor180of the artificial intelligence apparatus100may receive the speech recognition result generated from each of the external artificial intelligence apparatuses.

The speech recognition model may include an artificial neural network, and may be learned by using a machine learning algorithm or a deep learning algorithm. The speech recognition model may be separately trained for each external artificial intelligence apparatus.

Training data used to learn the speech recognition model may be set corresponding to an application environment of each external artificial intelligence apparatus. For example, a speech recognition model for a washing machine or a refrigerator serving as an artificial intelligence apparatus may be learned by using training data including a vibration noise. In addition, a speech recognition model for a TV, a radio, or a speaker serving as an artificial intelligence apparatus may be learned by using training data including media sounds such as music or movies. In addition, a speech recognition model for a cleaner, an air purifier, a fan, or an air conditioner serving as an artificial intelligence apparatus may be learned by using training data including wind sounds.

The speech recognition result may refer to the utterance voice converted into a text. In addition, the speech recognition result may include reliability for each word of the converted text. Hereinafter, a word may refer to both a single word as well as a plurality of words.

When an input feature vector extracted from the sound data is input, the speech recognition model may output words corresponding to the utterance voice and the reliability or a weight corresponding to each of the words in an output layer. This is because the speech recognition model determines a word having the highest reliability or the highest weight value output from the output layer as a word corresponding to the utterance voice.

Then, the processor180of the artificial intelligence apparatus100obtains the sound data including the utterance voice of the user through the microphone122(S703).

Similar to the external artificial intelligence apparatuses, the artificial intelligence apparatus100may obtain the sound data including the utterance voice of the user through the microphone122. The sound data obtained by the artificial intelligence apparatus100may include an utterance voice which is the same as an utterance voice included in sound data obtained by the external artificial intelligence apparatuses. Similarly, the obtained sound data may vary depending on the ambient noise, the spatial structure, the positional relation with the user, and the like.

The obtaining of the sound data may refer to converting a sound wave corresponding to the utterance voice of the user into a sound signal or sound data through the microphone122.

Then, the processor180of the artificial intelligence apparatus100generates a speech recognition result from the obtained sound data (S705).

The processor180may generate the speech recognition result from the sound data obtained by using the speech recognition model stored in the memory170.

The speech recognition model may include an artificial neural network, and may be learned by using a machine learning algorithm or a deep learning algorithm.

Training data used to train the speech recognition model may be set corresponding to an application environment of the artificial intelligence apparatus100. As described above, when the artificial intelligence apparatus100is a washing machine or a refrigerator, the speech recognition model may be learned by using training data including a vibration noise. In addition, when the artificial intelligence apparatus100is a TV, a radio, or a speaker, the speech recognition model may be learned by using training data including media sounds such as music or movies. In addition, when the artificial intelligence apparatus100is a cleaner, an air purifier, a fan, or an air conditioner, the speech recognition model may be learned by using training data including wind sounds.

The speech recognition result may refer to the utterance voice converted into a text. In addition, the speech recognition result may include reliability for each word of the converted text.

The processor180may extract an input feature vector for speech recognition from the obtained sound data, input the extracted input feature vector to the speech recognition model, and obtain words corresponding to the utterance voice output from the output layer of the speech recognition model and reliability or a weight corresponding to each of the words as the speech recognition result.

Then, the processor180of the artificial intelligence apparatus100determines an environment variable corresponding to a speech reception environment by using the obtained sound data (S707).

The environment variable may include at least one of a noise level, a noise type, an utterance level, or positional relation. A noise refers to a sound that is not the utterance voice of the user included in the sound data, and the noise type may include at least one of media, a vibration, a conversation, a wind, or a daily life noise. The positional relation may refer to positional relation between each of the artificial intelligence apparatuses and the user.

The processor180may determine the environment variable by using the sound data obtained through the microphone122or the sound data obtained from the external artificial intelligence apparatus. When using the sound data obtained by the external artificial intelligence apparatus, the processor180may receive the sound data from the external artificial intelligence apparatus through the communication unit110.

The processor180may determine the noise type from the sound data by using a noise classification model. The noise classification model may include an artificial neural network, and may be learned by using a machine learning algorithm or a deep learning algorithm.

The processor180may obtain a timestamp for a reception time of the sound data, receive the timestamp indicating the reception time of the utterance voice from each of the external artificial intelligence apparatuses through the communication unit110, use the timestamps to calculate a reception time difference between the artificial intelligence apparatuses, determine a location of the user by using the calculated reception time difference, and determine the positional relation based on the determined location of the user. As described above, the processor180may determine the positional relation by using a DOA algorithm.

In addition, the processor180of the artificial intelligence apparatus100generates a final speech recognition result for the utterance voice by using the speech recognition result received from the at least one external artificial intelligence apparatus, the generated speech recognition result, and the generated environment variable (S709).

Each of the speech recognition results may include words corresponding to the utterance voice of the user and reliability corresponding to each of the words.

The processor180may generate the final speech recognition result by combining words having high reliability among the words included in each of the speech recognition results.

The processor180may determine a weight for each of the speech recognition results by using the determined environment variable, and generate the final speech recognition result based on the determined weight. In other words, the processor180may generate the final speech recognition result by summing up weights for each of the speech recognition results by using the determined weight.

For example, assume that the utterance voice of the user is “good day”, the first speech recognition result represents the reliability of “good day” as 0.8 and the reliability of “good die” as 0.3, the second speech recognition result represents the reliability of “good day” as 0.5 and the reliability of “good die” as 0.5, the third speech recognition result represents the reliability of “good day” as 0.8 and the reliability of “good die” as 0.1, and the speech recognition results have the same weighs of ⅓, ⅓, and ⅓. In this case, the processor180may sum up the weights for each of the speech recognition results by using the weights to determine the reliability of “good day” as 0.7 and the reliability of “good die” as 0.3. Accordingly, the processor180may determine that the utterance voice of the user is “good day”.

The processor180may determine the weights for the speech recognition results by using the environment variable corresponding to the speech reception environment at the utterance time of the user.

The processor180may determine a distance between each of the artificial intelligence apparatuses and the user based on the positional relation, and increase a weight for each of the artificial intelligence apparatuses as the distance between each of the artificial intelligence apparatuses and the user is shorter.

The processor180may compare the application environment and the noise type of each of the artificial intelligence apparatuses, and increase a weight for the artificial intelligence apparatus having the application environment corresponding to the determined noise type.

For example, if the noise type includes the media, the processor180may increase a weight for a speech recognition result of an artificial intelligence apparatus such as a TV, a radio, or a speaker. In addition, when the noise type includes the vibration, the processor180may increase a weight for an artificial intelligence apparatus such as a refrigerator or a washing machine. In addition, when the noise type includes the wind, the processor180may increase a weight for an artificial intelligence apparatus such as a cleaner, an air cleaner, a fan, or an air conditioner.

In particular, the processor180may determine the weights for the speech recognition results (or the weights for the artificial intelligence apparatuses) by using a weight determination model. When an input feature vector including at least one of the noise level, the noise type, the utterance level, or the positional relation is input, the weight determination model may output a weight for each of the speech recognition results or each of the artificial intelligence apparatuses as an output vector.

The weight determination model may include an artificial neural network, and may be learned by using a machine learning algorithm or a deep learning algorithm. For example, the weight determination model may be learned to output a weight representing the highest speech recognition success rate when the environment variable (or the input feature vector) is given by using training data including a speech recognition success rate when the weights for the speech recognition results are given.

In one embodiment, each of the external artificial intelligence apparatuses may determine the environment variable, and the artificial intelligence apparatus100may receive the environment variable determined from each of the artificial intelligence apparatuses through the communication unit110. In addition, the processor180may determine the weights for the speech recognition results based on an environment variable determined for each of the external artificial intelligence apparatuses.

In particular, the processor180may determine the weight for each of the speech recognition results (or the weight for each of the artificial intelligence apparatuses) based on the noise level in each of the artificial intelligence apparatuses.

In other words, the environment variable is determined individually based on the sound data obtained from each of the external artificial intelligence apparatuses, and the weight of each of the speech recognition results is determined by using the determined environment variable, so that an actual reception environment of each of the external artificial intelligence apparatuses may be reflected more accurately.

Then, the processor180of the artificial intelligence apparatus100performs a control corresponding to the generated final speech recognition result (S711).

When the utterance voice of the user is a query, the processor180may provide a response corresponding to the query, and when the utterance voice of the user is a command, the processor180may perform the control corresponding to the command.

FIG. 8is a flowchart illustrating the method of recognizing the voice of the user according to one embodiment.

In detail,FIG. 8illustrates an embodiment in which the artificial intelligence server200constituting the artificial intelligence system1operates as the master artificial intelligence apparatus to recognize the utterance voice of the user.

The redundant description of the configuration explained with reference toFIG. 7will be omitted.

Referring toFIG. 8, the processor260of the artificial intelligence server200receives the speech recognition result for the utterance voice of the user from each of a plurality of artificial intelligence apparatuses through the communication unit210(S801).

The artificial intelligence apparatus is an apparatus having a microphone to perform a function of recognizing an utterance voice of a user, and refers to a slave artificial intelligence apparatus. Meanwhile, the artificial intelligence server200that receives speech recognition results from the artificial intelligence apparatuses refers to a master artificial intelligence apparatus.

Each of the artificial intelligence apparatuses may obtain the sound data including the utterance voice of the user, and generate the speech recognition result from the obtained sound data. In addition, the artificial intelligence server200may receive the speech recognition result generated from each of the artificial intelligence apparatuses.

Then, the processor260of the artificial intelligence server200receives an environment variable corresponding to a speech reception environment from each of the artificial intelligence apparatuses through the communication unit210(S803).

Each of the artificial intelligence apparatuses may determine the environment variable corresponding to the speech reception environment at the time of receiving the utterance voice of the user, and the artificial intelligence server200may receive the environment variable determined from each of the artificial intelligence apparatuses through the communication unit210.

In addition, the processor260of the artificial intelligence server200generates a final speech recognition result by using the received speech recognition result and the received environment variable (S805).

The processor260may determine a weight for the speech recognition result generated by each of the artificial intelligence apparatuses based on the environment variable for each of the artificial intelligence apparatuses, and generate the final speech recognition result by using the received speech recognition result and the determined weight.

Then, the processor260of the artificial intelligence server200performs a control corresponding to the generated final speech recognition result (S807).

When the utterance voice of the user is a query, the processor260may provide a response corresponding to the query, and when the utterance voice of the user is a command, the processor260may perform the control corresponding to the command.

FIG. 9is a view illustrating a method of generating a final speech recognition result according to one embodiment.

Referring toFIG. 9, when a user901performs an utterance902as “Turn off the kitchen light.”, each of a TV911serving as a first artificial intelligence apparatus, a robot912serving as a second artificial intelligence apparatus, and an air conditioner913serving as a third artificial intelligence apparatus may obtain sound data including the utterance voice902of the user901.

InFIG. 9, the master artificial intelligence apparatus may be one of the first artificial intelligence apparatus911, the second artificial intelligence apparatus912, or the third artificial intelligence apparatus913, or may be an artificial intelligence server which is not shown inFIG. 9.

The master artificial intelligence apparatus may determine a weight930for the speech recognition result generated by each of the artificial intelligence apparatuses based on the obtained sound data.

In addition, the first artificial intelligence apparatus911may generate the speech recognition result for the utterance voice902of the user901from the sound data by using a first speech recognition model921. In addition, the second artificial intelligence apparatus912may generate the speech recognition result for the utterance voice902of the user901from the sound data by using a second speech recognition model922. In addition, the third artificial intelligence apparatus913may generate the speech recognition result for the utterance voice902of the user901from the sound data by using a third speech recognition model923.

Further, the master artificial intelligence apparatus may generate a final speech recognition result940from the speech recognition results generated by each of the artificial intelligence apparatuses911,912, and913by using the determined weights931,932, and933.

As shown inFIG. 9, each of the artificial intelligence apparatuses is provided with a speech recognition model. In addition, the speech recognition model may be different for each of the artificial intelligence apparatuses in which the speech recognition model is provided.

Each of the speech recognition models may be learned by using a training data set that takes into consideration an application environment of the artificial intelligence apparatus to be provided with the speech recognition model.

For example, a speech recognition model provided in an artificial intelligence apparatus such as a cleaner, a washing machine, or a refrigerator that generates a vibration may be learned by using a training data set including a vibration or a motor noise. In addition, a speech recognition model provided in an artificial intelligence apparatus such as a TV, a radio, or a speaker that plays media may be learned by using a training data set including various media noises. In addition, a speech recognition model provided in an artificial intelligence apparatus such as a cleaner, an air conditioner, or a fan that generates a wind noise may be learned by using a training data set including a wind sound. In addition, a speech recognition model provided in an artificial intelligence apparatus such as a robot or a smartphone that does not generate a large noise may be learned by using a clean training data set that does not include a noise.

Similarly, for example, the speech recognition model provided in the artificial intelligence apparatus such as the TV or the air conditioner located at a long distance (e.g., 3 m or more) from the user who utters may be learned by using a training data set including a long-distance utterance voice. In addition, the speech recognition model provided in the artificial intelligence apparatus such as the refrigerator, the washing machine, or the robot located at a medium/short distance (e.g., 1 m to 3 m) from the user who utters may be learned by using a training data set including a medium/short-distance utterance voice. In addition, the speech recognition model provided in the artificial intelligence apparatus such as the smartphone located at a short distance (e.g., 1 m or less) from the user who utters may be learned by using a training data set including a short-distance utterance voice.

FIGS. 10 and 11are views illustrating examples of a noise classification model according to one embodiment.

Referring toFIG. 10, sound data1010is input to a noise classification model1020. In detail, an input feature vector is extracted from the sound data1010, and the extracted input feature vector is input to an input layer of the noise classification model1020.

In addition, the noise classification model1020may output a value1030representing a type of a noise included in the sound data1010from an output layer. In detail, the noise classification model1020illustrated inFIG. 10may output only one value1030representing the noise type having the largest volume among the noise included in the sound data1010.

For example, an output value of 0 may represent that the noise type is the media, an output value of 1 may represent that the noise type is the vibration, an output value of 2 may represent that the noise type is the conversation, an output value of 3 may represent that the noise type is the wind, and an output value of 4 may represent that the noise type is the daily life noise.

Referring toFIG. 11, sound data1110is input to a noise classification model1120. In detail, an input feature vector is extracted from the sound data1110, and the extracted input feature vector is input to an input layer of the noise classification model1120.

In addition, the noise classification model1120may output a value1130representing a type of a noise included in the sound data1110from an output layer. In detail, the noise classification model1120illustrated inFIG. 11may output the value1130representing whether the noise included in the sound data1110corresponds to each noise type. In other words, a first output node of the output layer may output whether the sound data1110includes a noise of a first noise type. Similarly, a second output node of the output layer may output whether the sound data1110includes a noise of a second noise type.

For example, a case where an output value of the first output node is 1 may represent that the sound data1110includes a noise of which the noise type is the media. In addition, a case where an output value of the second output node is 1 may represent that the sound data1110includes a noise of which the noise type is the vibration. In addition, a case where an output value of a third output node is 1 may represent that the sound data1110includes a noise of which the noise type is the conversation. In addition, a case where an output value of a fourth output node is 1 may represent that the sound data1110includes a noise of which the noise type is the wind. In addition, a case where an output value of a fifth output node is 1 may represent that the sound data1110includes a noise of which the noise type is the daily life noise.

In the noise classification model1020illustrated inFIG. 10, the noise included in the sound data1010or1110is classified into only one noise type. However, in the noise classification model1120illustrated inFIG. 11, the noise included in the sound data1010or1110may be classified into a plurality of noise types. Accordingly, when the noise classification model1120illustrated inFIG. 11is used, the noise type may be more accurately determined for the sound data1010or1110including various noises, and a weight of each speech recognition result is determined based on the determined noise type, so that speech recognition accuracy may be increased.

According to various embodiments, the weight is determined in consideration of speech recognition characteristics of each of the artificial intelligence apparatuses, and the speech recognition results are collected based on the determined weight, so that a speech recognition rate can be increased.

According to an embodiment of the present invention, the above-described method may be implemented as a processor-readable code in a medium where a program is recorded. Examples of a processor-readable medium may include read-only memory (ROM), random access memory (RAM), CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.