Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. Narrative analytics that are linked to communication goal statements can employ a conditional outcome framework that allows the content and structure of resulting narratives to intelligently adapt as a function of the nature of the data under consideration. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal.

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. Narrative analytics that are linked to communication goal statements can employ a conditional outcome framework that allows the content and structure of resulting narratives to intelligently adapt as a function of the nature of the data under consideration. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal.

A method of detecting live speech comprises: receiving a signal containing speech; obtaining a first component of the received signal in a first frequency band, wherein the first frequency band includes audio frequencies; and obtaining a second component of the received signal in a second frequency band higher than the first frequency band. Then, modulation of the first component of the received signal is detected; modulation of the second component of the received signal is detected; and the modulation of the first component of the received signal and the modulation of the second component of the received signal are compared. It may then be determined that the speech may not be live speech, if the modulation of the first component of the received signal differs from the modulation of the second component of the received signal.

A learning apparatus includes: a speaker vector learning unit configured to learn a speaker vector extraction parameter λ based on one or more items of learning speech voice data in a speaker vector voice database; a non-speaker-individuality sound model learning unit configured to create a probability distribution model using a frequency component of one or more items of non-speaker-individuality sound data in a non-speaker-individuality sound database and calculate an internal parameter of the probability distribution model; and an age level estimation model learning unit configured to extract a speaker vector from voice data in an age level estimation model-learning voice database using the speaker vector extraction parameter λ, calculate a non-speaker-individuality sound likelihood vector from voice data in the age level estimation model-learning voice database using the internal parameters μ and Σ, and learn, with input of the speaker vector and the non-speaker-individuality sound likelihood vector, a parameter Ω of an age level estimation model that outputs an estimated value of an age level of a corresponding speaker.

A learning apparatus includes: a speaker vector learning unit configured to learn a speaker vector extraction parameter λ based on one or more items of learning speech voice data in a speaker vector voice database; a non-speaker-individuality sound model learning unit configured to create a probability distribution model using a frequency component of one or more items of non-speaker-individuality sound data in a non-speaker-individuality sound database and calculate an internal parameter of the probability distribution model; and an age level estimation model learning unit configured to extract a speaker vector from voice data in an age level estimation model-learning voice database using the speaker vector extraction parameter λ, calculate a non-speaker-individuality sound likelihood vector from voice data in the age level estimation model-learning voice database using the internal parameters μ and Σ, and learn, with input of the speaker vector and the non-speaker-individuality sound likelihood vector, a parameter Ω of an age level estimation model that outputs an estimated value of an age level of a corresponding speaker.

Embodiments of this application provide a voice interaction method and an electronic device, and relate to the field of artificial intelligence AI technologies and the field of voice processing technologies. A specific solution includes: An electronic device may receive first voice information sent by a second user, and the electronic device recognizes the first voice information in response to the first voice information. The first voice information is used to request a voice conversation with a first user. The electronic device may have, on a basis that the electronic device recognizes that the first voice information is voice information of the second user, a voice conversation with the second user by imitating a voice of the first user and in a mode in which the first user has a voice conversation with the second user.

Embodiments of this application provide a voice interaction method and an electronic device, and relate to the field of artificial intelligence AI technologies and the field of voice processing technologies. A specific solution includes: An electronic device may receive first voice information sent by a second user, and the electronic device recognizes the first voice information in response to the first voice information. The first voice information is used to request a voice conversation with a first user. The electronic device may have, on a basis that the electronic device recognizes that the first voice information is voice information of the second user, a voice conversation with the second user by imitating a voice of the first user and in a mode in which the first user has a voice conversation with the second user.

An electronic device is provided. The electronic device includes a microphone configured to receive an audio signal including a voice of a user, a sensor configured to detect a vibration signal generated by the user, at least one processor, and a memory configured to store an instruction executable by the processor. The at least one processor may be configured to determine a noise level included in the audio signal, calculate a verification score based on the noise level, the audio signal, and the vibration signal, and perform speaker verification for the user based on the verification score.

An electronic device is provided. The electronic device includes a microphone configured to receive an audio signal including a voice of a user, a sensor configured to detect a vibration signal generated by the user, at least one processor, and a memory configured to store an instruction executable by the processor. The at least one processor may be configured to determine a noise level included in the audio signal, calculate a verification score based on the noise level, the audio signal, and the vibration signal, and perform speaker verification for the user based on the verification score.

An electronic apparatus is provided. The electronic apparatus includes a communication interface with communication circuitry, a memory configured to store at least one instruction and a processor, and the processor is configured to receive a first audio recognized as a wake up word by an external device from the external device, determine whether the first audio corresponds to the wake up word by analyzing the first audio, based on determining that the first audio does not correspond to the wake up word, obtain a neural network model for detecting a wake up word misrecognition based on the first audio, and transmit information regarding the neural network model to the external device.