Abstract:
A system and method is provided that authenticates a user using hybrid biometrics information, such as a user's image information, a user's voice information, etc. The user authentication method includes: acquiring a number of biometrics information; generating a number of authentication information corresponding to the acquired biometrics information; and performing an integral user authentication based on the by generated authentication information.
Abstract:
Disclosed is an electronic device including a communication interface, a memory, a microphone, a speaker, a display, a main processor, and a sub-processor activating the main processor by recognizing a wake-up word included in a voice input. The at least one memory stores instructions that, when executed, cause the main processor to receive a first voice input to register the wake-up word, when the first voice input does not include a specified word, to receive a second voice input including a word identical to the first voice input, through the microphone, to generate a wake-up word recognition model for recognizing the wake-up word, and to store the generated wake-up word recognition model in the at least one memory, and when the first voice input includes the specified word, to output information for requesting a third voice input, through the speaker or the display.
Abstract:
Disclosed is an electronic device. The electronic device includes: a processor, and a memory operatively connected to the processor, the memory stores instructions that, when executed, cause the processor to: select at least one data received through a user input, analyze the selected data, extract additional data based on the analyzed data, learn a personal voice model using the data and the additional data, and provide response data using the personal voice model.
Abstract:
According to various example embodiments, an electronic device includes a microphone configured to receive an audio signal including speech of a user, a processor, and a memory configured to store instructions executable by the processor and personal information of the user, in which the processor is configured to extract a plurality of speech recognition candidates by analyzing a feature of the speech of the user, extract a keyword based on the plurality of speech recognition candidates, search for replacement data, based on the keyword and the personal information, and generate a recognition result corresponding to the speech of the user, based on the replacement data.
Abstract:
An electronic device and method are disclosed. The electronic device includes input circuitry, a display, and a processor. The processor implements the method, including extracting at least one piece of context information based at least in part on an application screen displayed on the display, analyzing the extracted at least one piece of context information to generate a language model based on the extracted at least one piece of context information, receiving a voice input of a user through the input circuitry and convert the voice input into a text string using the generated language model, and resetting the generated language model.
Abstract:
An electronic device configured to perform speaker verification on a voice input to determine whether the voice input matches a voice of an enrolled speaker, based on determining that the voice input does not match the voice of the enrolled speaker, perform first speech recognition on the voice input based on a first automatic speech recognition (ASR) model, and based on determining that the voice input matches the voice of the enrolled speaker, perform second speech recognition on the voice input based on a sequence summarizing neural network (SSN) and a second ASR model.
Abstract:
Disclosed is an electronic device including processor and memory operatively connected to the processor and storing language model. The electronic device may enter data into the language model, generate an embedding vector in the input embedding layer, add position information to the embedding vector in the positional encoding layer, branch the embedding vector based on domain information, normalize the branched embedding vectors, enter the normalized embedding vectors into the multi-head attention layer, enter output data of the multi-head attention layer into the first layer, normalize pieces of output data of the first layer, enter the normalized pieces of output data of the first layer into the feed-forward layer, enter output data of the feed-forward layer into the second layer and normalize pieces of output data of the second layer, and enter the normalized pieces of output data of the second layer into the linearization layer and the softmax layer to obtain result data. In addition, various embodiments as understood from the specification may be also possible.