Abstract: The present disclosure provides methods and devices for registering a voiceprint and authenticating a voiceprint. The method for registering a voiceprint includes performing a frame alignment operation on a registration character string inputted by a user in voice to extract first acoustic features of each first character constituting the registration character string; calculating a first posterior probability of the first acoustic features of each first character in a global Gaussian Mixture Model (GMM) model to perform a Baum-Welch (BW) statistic; extracting first vector features of each first character through a preset vector feature extractor configured for multi-character; and stitching the first vector features of each first character sequentially, to obtain a registration voiceprint model of the user.

Abstract: Disclosed are an artificial intelligence-based method and device for voiceprint authentication. The method comprises receiving a registration request of a user and providing a registration string to the user; receiving voice information of the user reading the registration string and producing N segments of voice on the basis of the voice information, where N is a positive integer; determining a gender tag of the user on the basis of a gender classification model and the N segments of voice; and producing a registration voiceprint model for the user on the basis of the gender tag and the N segments of voice.

Abstract: The present disclosure provides a method and a device for processing voiceprint authentication. The method includes: extracting a first feature vector for each first speech segment of a training set by a gender-mixed voiceprint baseline system based on Deep Neural Network; training a gender classifier according to the first feature vector for each first speech segment and a pre-labeled first gender label of each first speech segment; training Deep Neural Network models for different genders respectively according to speech data of different genders of the training set; and training uniform background models, feature vector extracting models and linear probability discriminant analysis models for different genders respectively according to the Deep Neural Network models for different genders and the speech data of different genders of the training set. A voiceprint authentication processing model for gender distinguishing is built, thus improving the efficiency and accuracy of voiceprint authentication.

Abstract: The present disclosure provides a voiceprint authentication method based on deep learning, a terminal and a non-transitory computer readable storage medium. The method includes: receiving a voice from a speaker; extracting a d-vector feature of the voice; obtaining a determined d-vector feature of the speaker during a registration stage; calculating a matching value between the d-vector feature and the determined d-vector feature; and determining that the speaker passes authentication when the matching value is greater than or equal to a threshold.

Abstract: The present disclosure provides methods and devices for registering a voiceprint and authenticating a voiceprint. The method for registering a voiceprint includes performing a frame alignment operation on a registration character string inputted by a user in voice to extract first acoustic features of each first character constituting the registration character string; calculating a first posterior probability of the first acoustic features of each first character in a global Gaussian Mixture Model (GMM) model to perform a Baum-Welch (BW) statistic; extracting first vector features of each first character through a preset vector feature extractor configured for multi-character; and stitching the first vector features of each first character sequentially, to obtain a registration voiceprint model of the user.

Abstract: The present disclosure provides a method and a device for processing voiceprint authentication. The method includes: extracting a first feature vector for each first speech segment of a training set by a gender-mixed voiceprint baseline system based on Deep Neural Network; training a gender classifier according to the first feature vector for each first speech segment and a pre-labeled first gender label of each first speech segment; training Deep Neural Network models for different genders respectively according to speech data of different genders of the training set; and training uniform background models, feature vector extracting models and linear probability discriminant analysis models for different genders respectively according to the Deep Neural Network models for different genders and the speech data of different genders of the training set. A voiceprint authentication processing model for gender distinguishing is built, thus improving the efficiency and accuracy of voiceprint authentication.

Abstract: Disclosed are an artificial intelligence-based method and device for voiceprint authentication. The method comprises receiving a registration request of a user and providing a registration string to the user; receiving voice information of the user reading the registration string and producing N segments of voice on the basis of the voice information, where N is a positive integer; determining a gender tag of the user on the basis of a gender classification model and the N segments of voice; and producing a registration voiceprint model for the user on the basis of the gender tag and the N segments of voice.