Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.

Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.

According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

For example, the accuracy of voice recognition is improved.

A signal processing device includes: a single speech detection unit that detects whether one channel of an input voice signal is a speech of a single speaker; a cluster information updating unit that updates cluster information based on a voice feature quantity when the input voice signal is a speech of a single speaker; a voice segment detection unit that detects a speech segment of a target speaker based on the cluster information; and a voice extraction unit that extracts only the voice signal of the target speaker from a mixed voice signal containing the voice of the target speaker.

For example, the accuracy of voice recognition is improved.

A signal processing device includes: a single speech detection unit that detects whether one channel of an input voice signal is a speech of a single speaker; a cluster information updating unit that updates cluster information based on a voice feature quantity when the input voice signal is a speech of a single speaker; a voice segment detection unit that detects a speech segment of a target speaker based on the cluster information; and a voice extraction unit that extracts only the voice signal of the target speaker from a mixed voice signal containing the voice of the target speaker.

Provided is a signal processing device including a main speech detection unit configured to detect, by using a neural network, whether or not a signal input to a sound collection device assigned to each of at least two speakers includes a main speech that is a voice of the corresponding speaker, and output frame information indicating presence or absence of the main speech.

Provided is a signal processing device including a main speech detection unit configured to detect, by using a neural network, whether or not a signal input to a sound collection device assigned to each of at least two speakers includes a main speech that is a voice of the corresponding speaker, and output frame information indicating presence or absence of the main speech.

Disclosed is an electronic device. The electronic device comprises: a microphone comprising circuitry; a speaker comprising circuitry; and a processor electrically connected to the microphone and speaker, wherein the processor, when a first user's voice is input through the microphone, identifies a user who uttered the first user's voice and provides a first response sound, which is obtained by inputting the first user's voice to an artificial intelligence model learned through an artificial intelligence algorithm, through the speaker, and when a second user's voice is input through the microphone, identifies a user who uttered the second user's voice, and if the user who uttered the first user's voice is the same as the user who uttered the second user's voice, provides a second response sound, which is obtained by inputting the second user's voice and utterance history information to the artificial intelligence model, through the speaker. In particular, at least some of the methods of providing a response sound to a user's voice may use an artificial intelligence model learned in accordance with at least one of a machine learning, neural network, or deep learning algorithm.

Disclosed is an electronic device. The electronic device comprises: a microphone comprising circuitry; a speaker comprising circuitry; and a processor electrically connected to the microphone and speaker, wherein the processor, when a first user's voice is input through the microphone, identifies a user who uttered the first user's voice and provides a first response sound, which is obtained by inputting the first user's voice to an artificial intelligence model learned through an artificial intelligence algorithm, through the speaker, and when a second user's voice is input through the microphone, identifies a user who uttered the second user's voice, and if the user who uttered the first user's voice is the same as the user who uttered the second user's voice, provides a second response sound, which is obtained by inputting the second user's voice and utterance history information to the artificial intelligence model, through the speaker. In particular, at least some of the methods of providing a response sound to a user's voice may use an artificial intelligence model learned in accordance with at least one of a machine learning, neural network, or deep learning algorithm.