Patent Description:
Recently, to classify an input pattern as a member that belongs to a group, researchers are actively conducting research on methods of applying efficient and accurate pattern recognition. One such area of research is focused on an artificial neural network that models characteristics for pattern recognition by mathematical expressions. To classify an input pattern as a member that belongs to a group, the neural network employs an algorithm. Through this algorithm, the neural network may generate a mapping between the input pattern and output patterns. The capability of generating such a mapping may be referred to as a learning capability of the neural network. Further, the neural network may have a generalization capability of generating a relatively accurate output with respect to an input pattern that is yet to be used for learning, based on a result of prior learning.

In the paper entitled 'Speaker Model Clustering to Construct Background Models for Speaker Verification' of Gokay DISKEN, ZEkeriya TUFEKCI, Ulus CEVIK, are disclosed speaker models that are clustered to obtain better imposter model representations for speaker verification purpose.

The invention is defined in the appended independent claims <NUM>, <NUM> and <NUM>. Preferred embodiments of the invention are set out in the appended dependent claims.

In one general aspect, there is provided a user verification method including steps defined in the appended independent method claim.

In another general aspect, there is provided a user verification apparatus including features defined in the appended independent apparatus claim.

When a part is connected to another part, it includes not only a case where the part is directly connected but also a case where the part is connected with another part in between. Also, when a part includes a constituent element, other elements may also be included in the part, instead of the other elements being excluded, unless specifically stated otherwise. Although terms such as "first," "second," "third" "A," "B," (a), and (b) may be used herein to describe various members, components, regions, layers, or sections, these members, components, regions, layers, or sections are not to be limited by these terms.

It should be noted that if it is described that one component is "connected", "coupled", or "joined" to another component, a third component may be "connected", "coupled", and "joined" between the first and second components, although the first component may be directly connected, coupled, or joined to the second component. However, if the specification states that a first component is "directly connected" or "directly joined" to a second component, a third component may not be "connected" or "joined" between the first component and the second component. Similar expressions, for example, "between" and "immediately between" and "adjacent to" and "immediately adjacent to," are also to be construed in this manner.

The terminology used herein is for describing various examples only, and is not to be used to limit the disclosure or claims.

The use of the term 'may' herein with respect to an example or embodiment, e.g., as to what an example or embodiment may include or implement, means that at least one example or embodiment exists where such a feature is included or implemented while all examples and embodiments are not limited thereto.

<FIG> is a diagram illustrating an example of a user verification apparatus using a generalized user model.

Referring to <FIG>, a user verification apparatus <NUM> receives input data and generates output data by processing the received input data. The input data may correspond to an indicia used for identification, such as, for example, input voice, input biometric information, or an input image. For example, in a case of speaker verification, the input data may include a voice or an audio, and may be referred to as speech data. In a case of face verification, the input data may include a face image. In a case of fingerprint verification, the input data may include a fingerprint image. In a case of iris verification, the input data may include an iris image.

The input data may be input by a test user attempting user verification. The user verification apparatus <NUM> generates a result of verification of the test user by processing the input data. Output data may include a result of the verification the test user generated by the user verification apparatus <NUM>. For example, the verification result may indicate a verification success or indicate a verification failure.

In an example, the user verification apparatus <NUM> uses a feature extractor <NUM> and comparators <NUM> and <NUM> to perform the user verification. Each of the feature extractor <NUM>, the comparator <NUM>, and the comparator <NUM> may be implemented on a hardware module. For example, each of the feature extractor <NUM>, the comparator <NUM>, and the comparator <NUM> may be implemented as a neural network on an appropriate hardware.

In an example, the neural network may correspond to a recurrent neural network (RNN) or a convolutional neural network (CNN). In an example, the CNN may be a deep neural network (DNN). Ain an example, the DNN may include a fully-connected network (FCN), a deep convolutional network (DCN), a long-short term memory (LSTM) network, and a grated recurrent units (GRUs). The DNN may include a plurality of layers. The plurality of layers may include an input layer, at least one hidden layer, and an output layer. In an example, neural network may include a sub-sampling layer, a pooling layer, a fully connected layer, etc., in addition to a convolution layer.

The neural network may map input data and output data that have a nonlinear relationship based on deep learning to perform tasks such as, for example, object classification, object recognition, audio or speech recognition, and image recognition. The deep learning may be a type of machine learning that is applied to perform image recognition or speech recognition from a big dataset. The deep learning may be performed in supervised and/or unsupervised manners, which may be applied to perform the mapping of input data and output data.

The neural network may be trained to perform a desired operation by mapping input data and output data that have a nonlinear relationship therebetween through deep learning to perform tasks such as, for example, object classification, object recognition, audio or speech recognition, and image recognition. The deep learning is a machine learning method used to solve a problem given from a big dataset. The deep learning may also be construed as a problem-solving process for optimization to find a point where energy is minimized while training the neural network using provided training data. Through the deep learning, for example, supervised or unsupervised learning, a weight corresponding to an architecture or a model of the neural network may be obtained, and the input data and the output data may be mapped to each other based on the obtained weight.

In an example, the neural network may be implemented as an architecture having a plurality of layers including an input image, feature maps, and an output. In the neural network, a convolution operation between the input image, and a filter referred to as a kernel, is performed, and as a result of the convolution operation, the feature maps are output. Here, the feature maps that are output are input feature maps, and a convolution operation between the output feature maps and the kernel is performed again, and as a result, new feature maps are output. Based on such repeatedly performed convolution operations, results of recognition of characteristics of the input image via the neural network may be output.

In another example, the neural network may include an input source sentence, (e.g., voice entry) instead of an input image. In such an example, a convolution operation is performed on the input source sentence with a kernel, and as a result, the feature maps are output. The convolution operation is performed again on the output feature maps as input feature maps, with a kernel, and new feature maps are output. When the convolution operation is repeatedly performed as such, a recognition result with respect to features of the input source sentence may be finally output through the neural network.

The feature extractor <NUM> generates a test feature vector corresponding to the test user based on input data that is input by the test user. The feature extractor <NUM> generates the test feature vector by extracting at least one feature from the input data. For example, in the case of speaker verification, the feature extractor <NUM> extracts a feature from speech data input by the test user and generates a test feature vector corresponding to the test user. The test feature vector includes information for identifying the test user. In an example, the feature extractor <NUM> includes a neural network trained to extract features from a network input.

The comparator <NUM> determines a first parameter indicating a similarity between the test feature vector and an enrolled feature vector and outputs the determined first parameter. The first parameter increases as a distance between the test feature vector and the enrolled feature vector decreases. The comparator <NUM> includes a neural network trained to output a similarity between the enrolled feature vector and a network input.

The enrolled feature vector is enrolled for the user verification in advance. The enrolled feature vector is enrolled by an enrolled user and includes information for identifying the enrolled user. The enrolled feature vector includes at least one feature extracted from enrolled data that is input to enroll the enrolled user. The data is input by the enrolled user. The enrolled feature vector corresponding to an enrolled user is generated by extracting a feature from speech data input by the enrolled user. When it is determined that the test user matches the enrolled user, a verification of the test user is successful. When it is determined that the test user does not match the enrolled user, the verification of the test user fails.

The comparator <NUM> determines a second parameter indicating a similarity between the test feature vector and a user model corresponding to generalized users and outputs the determined second parameter. The second parameter increases as a distance between the test feature vector and the user model decreases. The user model includes data corresponding to the generalized users. The user model includes generalized feature vectors corresponding to the generalized users. The similarity between the test feature vector and the user model is determined based on distances between the test feature vector and the generalized feature vectors.

For example, data corresponding to the generalized users may be constructed based on at least a portion of data corresponding to a population of users. The user model may correspond to people other than the test user, that is, other users. The comparator <NUM> includes a neural network trained to output a similarity between the user model and a network input. The data corresponding to the generalized users corresponds to training data used in a process of training the neural network included in the comparator <NUM>.

Depending on an example, the user model may be stored as generalized feature vectors, stored as a cluster into which the generalized feature vectors are clustered, stored as a GMM, or stored through an application to a neural network. Clustering, GMM, and user model-based neural networks will be described in detail later.

The user verification apparatus <NUM> generates a verification result of the test user based on the first parameter and the second parameter. In an example, the user verification apparatus <NUM> determines a confidence score based on the first parameter and the second parameter and generates the verification result of the test user by comparing the confidence score and a threshold. In an example, when the confidence score is higher than the threshold, a verification result indicating a verification success is generated. In an example, when the confidence score is lower than the threshold, a verification result indicating a verification failure is generated. In an example, when the confidence score is equal to the threshold, whether a verification is to be processed as success or failure is determined by policy.

In the user verification, not only the enrolled feature vector but also the user model is taken into consideration, so that a verification result matching a characteristic of the test user is generated. In other words, a variable verification condition may be applied for each test user. In an example, when the test user has a general characteristic, the user model may be present at a distance close to the test feature vector, and thus, a tight verification condition may be applied to the test user. In another example, when the test user has a unique characteristic, the user model may be present at a distance far from the test feature vector, and thus, a loose verification condition may be applied to the test user.

User identification is a process of determining a test user. User verification is a process of determining whether a test user matches an enrolled user. Generally, a neural network trained in view of the user identification is used in the user verification. In this case, a characteristic obtained based on the user verification is not applied to a verification process. In the present disclosure, however, a reliability of the user verification may be improved by considering a relationship between a test user and another user through the user model in the user verification.

<FIG> is a diagram illustrating an example of controlling a verification condition.

Referring to <FIG>, a fixed verification condition is applied to a case <NUM> and a variable verification condition is applied to a case <NUM>. In the case <NUM>, a distance between a test feature vector x and an enrolled feature vector y does not satisfy a verification condition and thus, verification using the test feature vector x fails.

In the case <NUM>, similarities between the test feature vector x and generalized feature vectors c<NUM> through cn corresponding to a user model are relatively low and a verification condition is relaxed accordingly. Thus, in case <NUM>, a distance between the test feature vector x and the enrolled feature vector y satisfies the verification condition, and verification using the test feature vector x is successful. According to the case <NUM>, unnecessary verification attempts of a legitimate test user corresponding to the enrolled user may be reduced.

In an example, the verification condition may include a confidence score higher than a threshold. In an example, a second parameter indicating a similarity between a test feature vector and a user model is applied to the confidence score or applied to the threshold. As the second parameter increases, for example, as a distance between the test feature vector and the user model decreases, the confidence score decreases and the threshold increases. Since a probability of a verification success decreases as the confidence score decreases and the threshold increases, it can be seen that the verification condition is tightened. Conversely, since the probability of the verification success increases as the confidence score increases and the threshold decreases, it can be seen that the verification condition is relaxed. In an example, the confidence score is expressed by Equation <NUM>.

Equation <NUM> represents a posterior probability for a test user x and an overall user model θ including an enrolled user and a generalized user. In an example, equation <NUM> can be expressed as Equation <NUM>.

In an example, a user verification apparatus determines the confidence score based on a last term of Equation <NUM>. The last term of Equation <NUM> is rewritten as Equation <NUM>.

In Equation <NUM>, a numerator P(x|θc) denotes a posterior probability that the test user x corresponds to an enrolled user θc when the enrolled user θc exists. In an example, the test user x corresponds to a test feature vector and the enrolled user θc corresponds to an enrolled feature vector.

In Equation <NUM>, a denominator <MAT> denotes a posterior probability that the test user x corresponds to a user model θn when the user model θn exists. In an example, the user model θn corresponds to a user other than the test user x. The user model θn corresponds to the generalized feature vectors c<NUM> through cn.

P(x|θc) corresponds to a similarity between the test feature vector and the enrolled feature vector or corresponds to a distance between the test feature vector and the enrolled feature vector. For example, P(x|θc) may be determined based on a cosine distance between the test feature vector and the enrolled feature vector. The distance is a concept corresponding to a difference, and the similarity and the distance may be inversely proportional to each other. As the similarity between the test feature vector and the enrolled feature vector increases or as the distance between the test feature vector and the enrolled feature vector decreases, a value of P(x|θc) increases. Likewise, <MAT> corresponds to similarities or distances between the test feature vectors and the generalized feature vectors.

For ease and convenience, P(x|θc) and <MAT> will be described based on a concept of similarity. For example, in the following description, P(x|θc) corresponds to the first parameter indicating the similarity between the test feature vector and the enrolled feature vector, and <MAT> corresponds to the second parameter indicating the similarity between the test feature vector and the user model.

According to Equation <NUM>, the verification condition is expressed by Equation <NUM>.

In Equation <NUM>, TH denotes a threshold. Equation <NUM> represents a case in which the second parameter is applied to the confidence score. According to Equation <NUM>, the confidence score increases as the similarity between the test feature vector corresponding to the test user x and the enrolled feature vector corresponding to the enrolled user θc increases, and decreases as the similarity between the test feature vector and the user model θn increases.

According to Equation <NUM>, since the probability of the verification success decreases as the second parameter increases, it can be seen that the verification condition is tightened. Also, since the probability of the verification success increases as the second parameter decreases, it can be seen that the verification condition is relaxed. According to Equation <NUM>, the user verification apparatus determines the confidence score based on the first parameter and the second parameter and generates a verification result of the test user by comparing the confidence score and the threshold. Equation <NUM> can be expressed as Equation <NUM>.

Equation <NUM> represents a case in which the second parameter is applied to the threshold. According to Equation <NUM>, the threshold increases as the similarity between the test feature vector corresponding to the test user x and the user model θn increases.

A right hand side of Equation <NUM>, <MAT> may be defined as a new threshold. According to Equation <NUM>, since the probability of the verification success decreases as the second parameter increases, it can be seen that the verification condition is tightened. Also, since the probability of the verification success increases as the second parameter decreases, it can be seen that the verification condition is relaxed. According to Equation <NUM>, the user verification apparatus determines the threshold based on the second parameter and generates the verification result of the test user by comparing the confidence score corresponding to the first parameter and the determined threshold.

<FIG> is a diagram illustrating an example of tightening a verification condition. <FIG> illustrates an enrolled feature vector a and generalized feature vectors c1 through c8. The generalized feature vectors c1 through c8 correspond to a user model. Because similarities between the enrolled feature vector a and the generalized feature vectors c1 through c4 are relatively high, a verification condition is tightened.

For example, an original threshold TH may be adjusted to a new threshold TH*A based on a correction factor A. The threshold TF of <FIG> may correspond to the threshold of Equation <NUM> and Equation <NUM>. The correction factor A of <FIG> may correspond to <MAT> of Equation <NUM>. In an example, the correction factor A may be a real number greater than <NUM> and may increase the original threshold TH. Since a probability of a verification success decreases when the original threshold TH increases due to the correction factor A, it can be seen that the verification condition is tightened.

<FIG> is a diagram illustrating an example of relaxing a verification condition. <FIG> illustrates an enrolled feature vector b and generalized feature vectors c1 through c8. Because similarities between the enrolled feature vector b and neighboring generalized feature vectors c5 through c8 are relatively low, a verification condition is relaxed.

For example, an original threshold TH may be adjusted to a new threshold TH*B based on a correction factor B. The threshold TF of <FIG> may correspond to the threshold of Equation <NUM> and Equation <NUM>. The correction factor B of <FIG> may correspond to <MAT> of Equation <NUM>. For example, the correction factor B may be a real number less than <NUM> and may reduce the original threshold TH. Since a probability of a verification success increases when the original threshold TH decreases due to the correction factor B, it can be seen that the verification condition is relaxed.

<FIG> is a diagram illustrating an example of a clustering-based discrete user model. <FIG> illustrates an original user model <NUM> and a clustered user model <NUM>.

The original user model <NUM> includes generalized feature vectors c1 through c8. Although <FIG> illustrates eight feature vectors c1 through c8, a number of feature vectors included in the original user model <NUM> is not limited thereto. The feature vectors c1 through c8 are clustered into feature clusters θ1 through θ4 to reduce a computing load that is consumed to process operations associated with a plurality of feature vectors.

Also, a portion of the feature clusters θ1 through θ4 are selected as representative feature clusters θ1 through θ3 to be used for determining a second parameter. A number of feature clusters among the feature clusters θ1 through θ4 are selected as the representative feature clusters θ1 through θ3 in an order of adjacency to a test feature vector x. In an example, when a number of feature vectors may be <NUM>, a number of feature clusters may be <NUM>, <NUM>, or <NUM>, and a number of representative feature clusters may be <NUM>.

In an example, a user verification apparatus selects at least a portion of the feature clusters θ1 through θ4 to be the representative feature clusters θ1 through θ3 based on similarities between the test feature vector x and the feature clusters θ1 through θ4. In an example, the user verification apparatus determines the second parameter based on similarities between the test feature vector x and the representative feature clusters θ1 through θ3. For example, the user verification apparatus determines the second parameter by inserting the representative feature clusters θ1 through θ3 as the user model θn in the denominator <MAT> of Equation <NUM>. The user verification apparatus determines the confidence score by calculating the numerator P(x|θc) of Equation <NUM> based on the test feature vector x and the enrolled feature vector θc.

<FIG> is a diagram illustrating an example of a GMM-based continuous user model. <FIG> illustrates an original user model <NUM> and a GMM <NUM>. The GMM <NUM> includes a Gaussian distribution N(µn, σn) where µ denotes an average and σ denotes a standard deviation. In the example of <FIG>, the GMM <NUM> includes a first Gaussian distribution N(µ<NUM>, σ<NUM>) through a fourth Gaussian distribution N(µ<NUM>, σ<NUM>). The first Gaussian distribution N(µ<NUM>, σ<NUM>) through the fourth Gaussian distribution N(µ<NUM>, σ<NUM>) correspond to a distribution of generalized feature vectors c1 through c8. A user verification apparatus determines a second parameter based on a test feature vector x and the GMM <NUM>. For example, the user verification apparatus determines the second parameter by calculating the denominator <MAT> of Equation <NUM> using the Gaussian distributions and the test feature vector x. The user verification apparatus determines a confidence score by calculating the numerator P(x|θc) of Equation <NUM> based on the test feature vector x and an enrolled feature vector θc.

<FIG> is a diagram illustrating an example of a user model-based neural network. <FIG> illustrates a user model <NUM> and a neural network <NUM> trained based on the user model <NUM>. The neural network <NUM> is trained to output a similarity between a network input and the user model <NUM>. For example, the network input may be trained to output similarities between the network input and generalized feature vectors c1 through c8 included in the user model <NUM>. In this example, the neural network <NUM> may correspond to the comparator <NUM> of <FIG>, and an output of the neural network <NUM> may correspond to the denominator <MAT> of Equation <NUM>. The user verification apparatus inputs a test feature vector to the neural network <NUM> and acquires a second parameter corresponding to an output of the neural network <NUM>.

<FIG> is a diagram illustrating an example of a user verification apparatus.

Referring to <FIG>, a user verification apparatus <NUM> receives input data. In a case of speaker verification, the user verification apparatus <NUM> may also be referred to as a speaker verification apparatus. The input data may correspond to an input voice or an input image. The user verification apparatus <NUM> processes the input data using a neural network. For example, the user verification apparatus <NUM> may process a verification operation on the input data using the neural network. A database <NUM> stores an enrolled feature vector and a user model. A processor <NUM> uses the enrolled feature vector and the user model stored in the database <NUM> to perform user verification. In an example, the user model may be stored through clustering, stored as a GMM, or stored as the neural network <NUM> of <FIG>.

The user verification apparatus <NUM> performs at least one of the operations described herein for user verification, and provides a user with a result of the user verification. In an example, the result of the user verification is provided by an audio or video feedback through an input/output module (not shown). The user verification apparatus <NUM> includes at least one processor <NUM> and a memory <NUM>. The memory <NUM> is connected to the processor <NUM>, and stores instructions executable by the processor <NUM>, and data to be calculated by the processor <NUM> or data processed by the processor <NUM>. The memory <NUM> may include a non-transitory computer-readable medium, for example, a high-speed random-access memory (RAM), and/or a nonvolatile computer-readable storage medium, for example, at least one disk storage device, flash memory device, and other nonvolatile solid-state memory devices. Further details regarding the memory <NUM> is provided below.

The processor <NUM> refers to a data processing device configured as hardware with a circuitry in a physical structure to execute desired operations. For example, the desired operations may include codes or instructions included in a program. For example, the data processing device configured as hardware may include a microprocessor, a central processing unit (CPU), a processor core, a multicore processor, a reconfigurable processor, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a graphics processor unit (GPU), or any other type of multi- or single-processor configuration.

The processor <NUM> executes instructions to perform at least one of the operations described above with reference to <FIG>. For example, the processor <NUM> generates a test feature vector corresponding to a test user based on input data input by the test user, determine a first parameter indicating a similarity between the test feature vector and an enrolled feature vector enrolled for user verification, determine a second parameter indicating a similarity between the test feature vector and a user model corresponding to generalized users, and generates a verification result of the test user based on the first parameter and the second parameter. Further details regarding the processor <NUM> is provided below.

<FIG> is a diagram illustrating an example of an electronic device incorporating a user verification apparatus.

Referring to <FIG>, an electronic device <NUM> receives input data and processes a user verifying operation associated with the input data. The electronic device <NUM> uses the above-described user model to process the user verifying operation. The electronic device <NUM> may include the user verification apparatus described with reference to <FIG>, or perform functions of the user verification apparatus described with reference to <FIG>.

In an example, the electronic device <NUM>.

The electronic device <NUM> includes a processor <NUM>, a memory <NUM>, a camera <NUM>, a storage device <NUM>, an input device <NUM>, an output device <NUM>, and a network interface <NUM>. The processor <NUM>, the memory <NUM>, the camera <NUM>, the storage device <NUM>, the input device <NUM>, the output device <NUM>, and the network interface <NUM> may communicate with one another through a communication bus <NUM>.

The processor <NUM> refers to a data processing device configured as hardware with a circuitry in a physical structure to execute desired operations. For example, the desired operations may include codes or instructions included in a program. For example, the data processing device configured as hardware may include a microprocessor, a central processing unit (CPU), a processor core, a multicore processor, a reconfigurable processor, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a graphics processor unit (GPU), or any other type of multi- or single-processor configuration. The processor <NUM> executes functions and instructions in the electronic device <NUM>. For example, the processor <NUM> may process instructions stored in the memory <NUM> or the storage device <NUM>. The process <NUM> performs at least one of the operations described above with reference to <FIG>. Further details regarding the process <NUM> is provided below.

The memory <NUM> stores information used for performing user verification. The memory <NUM> includes a computer-readable storage medium or a computer-readable storage device. The memory <NUM> stores instructions to be executed by the processor <NUM>, and stores related information while a software program or an application is being executed by the electronic apparatus <NUM>. Further details regarding the memory <NUM> is provided below.

The camera <NUM> captures a still image, a video image, or both the images. The camera <NUM> captures an image of a face region input by a user for face verification. In an example, the camera <NUM> provides a three-dimensional (3D) image including depth information of objects.

The storage device <NUM> includes a computer-readable storage medium or a computer-readable storage device. The storage device <NUM> stores a database including information used for processing the user verification, such as, for example, an enrolled feature vector and a user model. For example, the storage device <NUM> may store a greater amount of information for a longer period of time, compared to the memory <NUM>. The storage device <NUM> may include, for example, a magnetic hard disk, an optical disc, a flash memory, a floppy disk, and other types of nonvolatile memory that are well-known in the related technical fields. Further details regarding the storage device <NUM> is provided below.

The input device <NUM> receives an input from a user through a traditional input method, for example, a keyboard and a mouse, and a new input method, for example, a touch input, a voice input, and an image input. The input device <NUM> may include, for example, a keyboard, a mouse, a touchscreen, a microphone, and other devices that may detect an input from a user and transmit the detected input to the electronic apparatus <NUM>.

The output device <NUM> provides an output of the electronic apparatus <NUM> to a user through a visual, auditory, or tactile channel. The output device <NUM> may include, for example, a display, a touchscreen, a speaker, a vibration generator, and other devices that may provide an output to a user. The network interface <NUM> communicates with an external device through a wired or wireless network.

In an example, the input device <NUM> and the output device <NUM> may be a display that receives an input from a user or provides an output. The user interface is a physical structure that includes one or more hardware components that provide the ability to render a user interface, render a display, and/or receive user input. However, the input device <NUM> and the output device <NUM> are not limited to the example described above, and any other displays, such as, for example, computer monitor and eye glass display (EGD) that are operatively connected to the electronic device <NUM> may be used without departing from the scope of the illustrative examples described.

<FIG> is a diagram illustrating an example of a user verification method. The operations in <FIG> may be performed in the sequence and manner as shown, although the order of some operations may be changed or some of the operations omitted without departing from the scope of the illustrative examples described. Many of the operations shown in <FIG> may be performed in parallel or concurrently. One or more blocks of <FIG>, and combinations of the blocks, can be implemented by special purpose hardware-based computer, such as a processor, that perform the specified functions, or combinations of special purpose hardware and computer instructions. In addition to the description of <FIG> below, the descriptions of <FIG> are also applicable to <FIG>, and are incorporated herein by reference. Thus, the above description may not be repeated here.

Referring to <FIG>, in operation <NUM>, a user verification apparatus generates a test feature vector corresponding to a test user based on input data that is input by the test user. In operation <NUM>, the user verification apparatus determines a first parameter indicating a similarity between the test feature vector and an feature vector that is enrolled for user verification. In operation <NUM>, the user verification apparatus determines a second parameter indicating a similarity between the test feature vector and a user model corresponding to generalized users. In operation <NUM>, the user verification apparatus generates a verification result of the test user based on the first parameter and the second parameter. In a case of speaker verification, the user verification method may also be referred to as a speaker verification method. In this case, operations <NUM> through <NUM> may be performed by a speaker verification apparatus. The speaker verification apparatus extracts a feature from speech data input by a test user and generates a test feature vector corresponding to the test user in operation <NUM>.

User verification apparatus <NUM>, feature extractor <NUM>, comparators <NUM> and <NUM>, user verification apparatus <NUM>, electronic device <NUM>, and other apparatuses, units, modules, devices, and other components described herein are implemented by hardware components. Examples of hardware components that may be used to perform the operations described in this application where appropriate include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more of the hardware components that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers. A processor or computer may be implemented by one or more processing elements, such as an array of logic gates, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field-programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices that is configured to respond to and execute instructions in a defined manner to achieve a desired result. In one example, a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer. Hardware components implemented by a processor or computer may execute instructions or software, such as an operating system (OS) and one or more software applications that run on the OS, to perform the operations described in this application. The hardware components may also access, manipulate, process, create, and store data in response to execution of the instructions or software. For simplicity, the singular term "processor" or "computer" may be used in the description of the examples described in this application, but in other examples multiple processors or computers may be used, or a processor or computer may include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components may be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components may be implemented by one or more processors, or a processor and a controller, and one or more other hardware components may be implemented by one or more other processors, or another processor and another controller. One or more processors, or a processor and a controller, may implement a single hardware component, or two or more hardware components. A hardware component may have any one or more of different processing configurations, examples of which include a single processor, independent processors, parallel processors, single-instruction single-data (SISD) multiprocessing, single-instruction multiple-data (SIMD) multiprocessing, multiple-instruction single-data (MISD) multiprocessing, and multiple-instruction multiple-data (MIMD) multiprocessing.

The methods that perform the operations described in this application are performed by computing hardware, for example, by one or more processors or computers, implemented as described above executing instructions or software to perform the operations described in this application that are performed by the methods.

Instructions or software to control a processor or computer to implement the hardware components and perform the methods as described above are written as computer programs, code segments, instructions or any combination thereof, for individually or collectively instructing or configuring the processor or computer to operate as a machine or special-purpose computer to perform the operations performed by the hardware components and the methods as described above. In an example, the instructions or software includes at least one of an applet, a dynamic link library (DLL), middleware, firmware, a device driver, an application program storing the method of outputting the state information. In one example, the instructions or software include machine code that is directly executed by the processor or computer, such as machine code produced by a compiler. In another example, the instructions or software include higher-level code that is executed by the processor or computer using an interpreter. Programmers of ordinary skill in the art can readily write the instructions or software based on the block diagrams and the flow charts illustrated in the drawings and the corresponding descriptions in the specification, which disclose algorithms for performing the operations performed by the hardware components and the methods as described above.

Examples of a non-transitory computer-readable storage medium include read-only memory (ROM), random-access programmable read only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), flash memory, card type memory such as multimedia card, secure digital (SD) card, or extreme digital (XD) card, magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, and any other device that is configured to store the instructions or software and any associated data, data files, and data structures in a non-transitory manner and providing the instructions or software and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the instructions.

Claim 1:
A computer-implemented user verification method comprising:
generating a feature vector corresponding to a user based on input data corresponding to the user or by extracting a feature from speech data corresponding to the user;
determining a first parameter, corresponding to a first confidence score, indicating a similarity between the feature vector and an enrolled feature vector enrolled for user verification in advance, wherein the enrolled feature vector corresponding to an enrolled user is generated by extracting a feature from speech data input by the enrolled user to enroll the enrolled user;
determining a second parameter indicating a similarity between the feature vector and a user model corresponding to generalized users; and
generating a verification result of the user based on the first parameter, a first threshold and the second parameter,
wherein generating the verification result of the user comprises:
determining a second confidence score by dividing the first parameter by the second parameter, and obtaining the verification result of the user by comparing the second confidence score and the first threshold; or
determining a second threshold by multiplying the first threshold with the second parameter, and generating the verification result of the user by comparing the first confidence score, corresponding to the first parameter, and the second threshold.