A linguistic content and speaking style disentanglement model includes a content encoder, a style encoder, and a decoder. The content encoder is configured to receive input speech as input and generate a latent representation of linguistic content for the input speech output. The content encoder is trained to disentangle speaking style information from the latent representation of linguistic content. The style encoder is configured to receive the input speech as input and generate a latent representation of speaking style for the input speech as output. The style encoder is trained to disentangle linguistic content information from the latent representation of speaking style. The decoder is configured to generate output speech based on the latent representation of linguistic content for the input speech and the latent representation of speaking style for the same or different input speech.

A linguistic content and speaking style disentanglement model includes a content encoder, a style encoder, and a decoder. The content encoder is configured to receive input speech as input and generate a latent representation of linguistic content for the input speech output. The content encoder is trained to disentangle speaking style information from the latent representation of linguistic content. The style encoder is configured to receive the input speech as input and generate a latent representation of speaking style for the input speech as output. The style encoder is trained to disentangle linguistic content information from the latent representation of speaking style. The decoder is configured to generate output speech based on the latent representation of linguistic content for the input speech and the latent representation of speaking style for the same or different input speech.

A computer includes a processor and a memory storing instructions executable by the processor to identify an occupant in a passenger cabin of a vehicle, detect a position of a head of the occupant relative to the passenger cabin, apply a first filter to speech from the occupant based on the position of the head, generate a second filter, apply the second filter to the speech, adjust the second filter based on a difference between the speech of the occupant filtered by the second filter and a prestored profile of the occupant, and perform an operation using the speech filtered by the first filter and the second filter.

A computer includes a processor and a memory storing instructions executable by the processor to identify an occupant in a passenger cabin of a vehicle, detect a position of a head of the occupant relative to the passenger cabin, apply a first filter to speech from the occupant based on the position of the head, generate a second filter, apply the second filter to the speech, adjust the second filter based on a difference between the speech of the occupant filtered by the second filter and a prestored profile of the occupant, and perform an operation using the speech filtered by the first filter and the second filter.

This application provides a speech signal processing method performed by a computer device. Through an iterative training process, a teacher speech separation model can play a smooth role in the training of a student speech separation model based on the accuracy of separation results of the student speech separation model of outputting a target speech signal from a mixed speech signal and the consistency between separation results obtained by the teacher speech separation model of outputting the target speech signal from the mixed speech signal and the student speech separation model of performing the same task, thereby maintaining the separation stability while improving the separation accuracy of the student speech separation model as a trained speech separation model, and greatly improving the separation capability of the trained speech separation model.

A speech separation model training method and apparatus, a computer-readable storage medium, and a computer device are provided, the method including: obtaining first audio and second audio, the first audio including target audio and having corresponding labeled audio, and the second audio including noise audio. obtaining an encoding model, an extraction model, and an initial estimation model; performing unsupervised training on the encoding model, the extraction model, and the estimation model according to the second audio, and adjusting model parameters of the extraction model and the estimation model; performing supervised training on the encoding model and the extraction model according to the first audio and the labeled audio corresponding to the first audio, and adjusting a model parameter of the encoding model; continuously performing the unsupervised training and the supervised training, so that the unsupervised training and the supervised training overlap, and the training is not finished until a training stop condition is met.

This application discloses an audio signal processing method performed by an electronic device. According to this application, embedding processing is performed on a mixed audio signal by mapping the mixed audio signal to an embedding space, to obtain an embedding feature of the mixed audio signal in the embedding space; and generalized feature extraction is performed on the embedding feature, so that a generalized feature of a target component in the mixed audio signal can be obtained through extraction. The generalized feature of the target component has good generalization capability and expression capability, and can be used for different scenarios. Audio signal processing is performed on the mixed audio signal based on the generalized feature of the target component to obtain information of the audio signal of the target object, thereby improving the robustness and generalization of an audio signal processing process, and improving the accuracy of audio signal processing.

This application discloses an audio signal processing method performed by an electronic device. According to this application, embedding processing is performed on a mixed audio signal by mapping the mixed audio signal to an embedding space, to obtain an embedding feature of the mixed audio signal in the embedding space; and generalized feature extraction is performed on the embedding feature, so that a generalized feature of a target component in the mixed audio signal can be obtained through extraction. The generalized feature of the target component has good generalization capability and expression capability, and can be used for different scenarios. Audio signal processing is performed on the mixed audio signal based on the generalized feature of the target component to obtain information of the audio signal of the target object, thereby improving the robustness and generalization of an audio signal processing process, and improving the accuracy of audio signal processing.

A method comprising determining at least one audio event based on an audio waveform and determining a deepfake probability for the audio event.

A method comprising determining at least one audio event based on an audio waveform and determining a deepfake probability for the audio event.