An authentication method. The method comprises comparing user voice data received via an electronic device to a stored voice template to determine a voice authentication parameter. A voice authentication threshold is determined and the voice authentication parameter is compared to the voice authentication threshold to determine whether to authenticate the user. Determining the voice authentication threshold comprises determining a current value of an enrolment counter, then comparing the current value of the enrolment counter to an enrolment counter threshold and determining whether the stored voice template is fully enrolled according to the result. If the stored voice template is fully enrolled, the voice authentication threshold is set to a first voice authentication threshold. If the stored voice template is not fully enrolled then a device attribute received from the electronic device is compared to a stored device attribute. If the received device attribute matches the stored device attribute, the voice authentication threshold is set to a second voice authentication threshold determined by the current value of the enrolment counter. If the received device attribute does not match the stored device attribute, the voice authentication threshold is set to a third voice authentication threshold.

An authentication method. The method comprises comparing user voice data received via an electronic device to a stored voice template to determine a voice authentication parameter. A voice authentication threshold is determined and the voice authentication parameter is compared to the voice authentication threshold to determine whether to authenticate the user. Determining the voice authentication threshold comprises determining a current value of an enrolment counter, then comparing the current value of the enrolment counter to an enrolment counter threshold and determining whether the stored voice template is fully enrolled according to the result. If the stored voice template is fully enrolled, the voice authentication threshold is set to a first voice authentication threshold. If the stored voice template is not fully enrolled then a device attribute received from the electronic device is compared to a stored device attribute. If the received device attribute matches the stored device attribute, the voice authentication threshold is set to a second voice authentication threshold determined by the current value of the enrolment counter. If the received device attribute does not match the stored device attribute, the voice authentication threshold is set to a third voice authentication threshold.

A server can receive a plurality of records at a databases such that each record is associated with a phone call and includes at least one request generated based on a transcript of the phone call. The server can generate a training dataset based on the plurality of records. The server can further train a binary classification model using the training dataset. Next, the server can receive a live transcript of a phone call in progress. The server can generate at least one live request based on the live transcript using a natural language processing module of the server. The server can provide the at least one live request to the binary classification model as input to generate a prediction. Lastly, the server can transmit the prediction to an entity receiving the phone call in progress. The prediction can cause a transfer of the call to a chatbot.

An account adding method is performed by a social networking application running at a mobile terminal when communicating with a second terminal (e.g., using a chat session). The method includes: recording voice information from the second terminal using the social networking application; extracting character string information and voiceprint information from the voice information; sending the character string information and the voiceprint information to a server; receiving an account that matches the character string information and the voiceprint information and that is sent by the server; and adding the account to a contact list of the social networking application. For example, the social networking application is started before starting a telephone call with the second terminal and the voice information is recorded during the telephone call.

A method of modelling speech of a user of a headset comprising a microphone, the method comprising: receiving a first sample, from a bone-conduction sensor, representing bone-conducted speech of the user; obtaining a measure of fundamental frequency of the bone-conducted speech in each of a plurality of speech frames of the first sample; obtaining a first distribution of the fundamental frequencies of the bone-conducted speech over the plurality of speech frames; receiving, from the microphone, a second sample; determining a first acoustic condition at the headset based on the second signal; performing a biometric process based on the first distribution of fundamental frequencies and the first acoustic condition.

A system and method may identify a fraud ring based on call or interaction data by analyzing by a computer processor interaction data including audio recordings to identify clusters of interactions which are suspected of involving fraud each cluster including the same speaker; analyzing by the computer processor the clusters, in combination with metadata associated with the interaction data, to identify fraud rings, each fraud ring describing a plurality of different speakers, each fraud ring defined by a set of speakers and a set of metadata corresponding to interactions including that speaker; and for each fraud ring, creating a relevance value defining the relative relevance of the fraud ring.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for verifying an identity of a user. The methods, systems, and apparatus include actions of receiving a request for a verification phrase for verifying an identity of a user. Additional actions include, in response to receiving the request for the verification phrase for verifying the identity of the user, identifying subwords to be included in the verification phrase and in response to identifying the subwords to be included in the verification phrase, obtaining a candidate phrase that includes at least some of the identified subwords as the verification phrase. Further actions include providing the verification phrase as a response to the request for the verification phrase for verifying the identity of the user.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for verifying an identity of a user. The methods, systems, and apparatus include actions of receiving a request for a verification phrase for verifying an identity of a user. Additional actions include, in response to receiving the request for the verification phrase for verifying the identity of the user, identifying subwords to be included in the verification phrase and in response to identifying the subwords to be included in the verification phrase, obtaining a candidate phrase that includes at least some of the identified subwords as the verification phrase. Further actions include providing the verification phrase as a response to the request for the verification phrase for verifying the identity of the user.

Provided in embodiments of the present application are a method and apparatus for user registration and electronic device. The method includes: after obtaining a wake-up voice of a user each time, extracting and storing a first voiceprint feature corresponding to the wake-up voice; clustering the stored first voiceprint features to divide the stored first voiceprint features into at least one category, wherein, each of the at least one category includes at least one first voiceprint feature which belongs to the same user; assigning one category identifier to each category; storing each category identifier in correspondence to at least one first voiceprint feature corresponding to this category identifier to complete user registration. The embodiments of the present application can simplify the user operation and improve the user experience.

Described herein are systems and methods for improved audio analysis using a computer-executed neural network having one or more in-network data augmentation layers. The systems described herein help ease or avoid unwanted strain on computing resources by employing the data augmentation techniques within the layers of the neural network. The in-network data augmentation layers will produce various types of simulated audio data when the computer applies the neural network on an inputted audio signal during a training phase, enrollment phase, and/or testing phase. Subsequent layers of the neural network (e.g., convolutional layer, pooling layer, data augmentation layer) ingest the simulated audio data and the inputted audio signal and perform various operations.