Embodiments of this application disclose a payment method, a client, an electronic device, a storage medium, and a server. The method includes: receiving a payment instruction of a user; generating, according to audio information in a voice input of the user, a voice feature vector of the audio information; performing matching between the voice feature vector and a user feature vector; and when the matching succeeds, sending personal information associated with the user feature vector to a server, so that the server performs a payment operation for a resource account associated with the personal information. The method can bring convenience to shopping by a consumer.

Embodiments of this application disclose a payment method, a client, an electronic device, a storage medium, and a server. The method includes: receiving a payment instruction of a user; generating, according to audio information in a voice input of the user, a voice feature vector of the audio information; performing matching between the voice feature vector and a user feature vector; and when the matching succeeds, sending personal information associated with the user feature vector to a server, so that the server performs a payment operation for a resource account associated with the personal information. The method can bring convenience to shopping by a consumer.

Provided are a method of authenticating a voice input provided from a user and a method of detecting a voice input having a strong attack tendency. The voice input authentication method includes: receiving the voice input; obtaining, from the voice input, signal characteristic data representing signal characteristics of the voice input; and authenticating the voice input by applying the obtained signal characteristic data to a first learning model configured to determine an attribute of the voice input, wherein the first learning model is trained to determine the attribute of the voice input based on a voice uttered by a person and a voice output by an apparatus.

Provided are a method of authenticating a voice input provided from a user and a method of detecting a voice input having a strong attack tendency. The voice input authentication method includes: receiving the voice input; obtaining, from the voice input, signal characteristic data representing signal characteristics of the voice input; and authenticating the voice input by applying the obtained signal characteristic data to a first learning model configured to determine an attribute of the voice input, wherein the first learning model is trained to determine the attribute of the voice input based on a voice uttered by a person and a voice output by an apparatus.

Systems, methods, and computer program products are provided for augmented authentication using acoustic devices. The method includes receiving a transfer request including an NFT identifier from one of one or more acoustic devices. The NFT identifier corresponds to an acoustic device NFT associated with the given acoustic device and a device user. The method includes comparing the NFT identifier with one or more stored NFT identifiers to determine the given acoustic device associated with the NFT identifier. The method further includes confirming that the identity of the voice command user matches the device user associated with the acoustic device. The method still further includes causing an authentication of the transfer request upon confirming the acoustic device is associated with the voice command user.

Systems, methods, and computer program products are provided for augmented authentication using acoustic devices. The method includes receiving a transfer request including an NFT identifier from one of one or more acoustic devices. The NFT identifier corresponds to an acoustic device NFT associated with the given acoustic device and a device user. The method includes comparing the NFT identifier with one or more stored NFT identifiers to determine the given acoustic device associated with the NFT identifier. The method further includes confirming that the identity of the voice command user matches the device user associated with the acoustic device. The method still further includes causing an authentication of the transfer request upon confirming the acoustic device is associated with the voice command user.

A system and method for real-time fraud detection with a social engineering phoneme (SEP) watchlist of phoneme sequences may perform real-time fraud prevention operations including receiving incoming call interactions and grouping the call interactions into one or more clusters, each cluster associated with a speaker's voice based on voiceprints. For a pair of voiceprints in a cluster, a phoneme sequence is extracted for each voice print. From the extracted phoneme sequences, a similarity score is then calculated to determine if a match exists between the extracted phoneme sequences based on a threshold. If determined a match exists, the phoneme sequence may be added to a SEP watchlist.

Systems and techniques for adaptive conversation support bot are described herein. An audio stream may be obtained including a conversation of a first user. An event may be identified in the conversation using the audio stream. A first keyword phrase may be extracted from the audio stream in response to identification of the event. The audio stream may be searched for a second keyword phrase based on the first keyword phrase. An action may be performed based on the first keyword phrase and the second keyword phrase. Results of the action may be out via a context appropriate output channel. The context appropriate output channel may be determined based on a context of the conversation and a privacy setting of the first user.

Systems and techniques for adaptive conversation support bot are described herein. An audio stream may be obtained including a conversation of a first user. An event may be identified in the conversation using the audio stream. A first keyword phrase may be extracted from the audio stream in response to identification of the event. The audio stream may be searched for a second keyword phrase based on the first keyword phrase. An action may be performed based on the first keyword phrase and the second keyword phrase. Results of the action may be out via a context appropriate output channel. The context appropriate output channel may be determined based on a context of the conversation and a privacy setting of the first user.

A method of speaker identification comprises receiving an audio signal representing speech; performing a first voice biometric process on the audio signal to attempt to identify whether the speech is the speech of an enrolled speaker; and, if the first voice biometric process makes an initial determination that the speech is the speech of an enrolled user, performing a second voice biometric process on the audio signal to attempt to identify whether the speech is the speech of the enrolled speaker. The second voice biometric process is selected to be more discriminative than the first voice biometric process.