Method and apparatus for training voiceprint recognition system

An apparatus for training a voiceprint recognition system, executed by a computer system, comprising:

• a categorization module, configured to obtain a voice training data set, the voice training data set comprising a plurality of voice segments of a plurality of users;
• determine identity vectors of all the voice segments in the voice training data set;
• identify identity vectors of a plurality of voice segments of a same user in the determined identity vectors;
• and place the recognized identity vectors of the same user in the plurality of users into one of a plurality of user categories;
• and
• a determining module, configured to determine an identity vector in the user category as a first identity vector;
• normalize the first identity vector by using a normalization matrix, a first value being a sum of similarity degrees between the first identity vector normalized by using the normalization matrix in the corresponding category and the other identity vectors normalized by using the normalization matrix in the corresponding category;
• and train the normalization matrix, and output a training value of the normalization matrix when the normalization matrix maximizes a sum of first values of all the user categories.

A non-transitory computer readable storage medium storing a computer program, and the computer program, when being executed by a processor, cause the processor to perform:

• obtaining a voice training data set, the voice training data set comprising a plurality of voice segments of a plurality of users;
• determining identity vectors of all the voice segments in the voice training data set;
• identifying identity vectors of a plurality of voice segments of a same user in the determined identity vectors;
• placing the recognized identity vectors of the same user in the plurality of users into one of a plurality of user categories;
• determining an identity vector in the user category as a first identity vector;
• normalizing the first identity vector by using a normalization matrix, a first value being a sum of similarity degrees between the first identity vector normalized by using the normalization matrix in the corresponding category and other identity vectors normalized by using the normalization matrix in the corresponding category;
• and
• training the normalization matrix, and outputting a training value of the normalization matrix when the normalization matrix maximizes a sum of first values of all the user categories.