Dynamic security code speech-based identity authentication system and method having self-learning function

A dynamic security code speech-based identity authentication system having self-learning function, comprising:

• a request receiving module for receiving an identity authentication request that a requester sends to a server through a client;
• a dynamic security code generating module for generating a dynamic security code and sending the dynamic security code to the client;
• and
• an identity authentication module for calculating a comprehensive confidence of an identity of the requester by using an acoustic model of global characters and a voiceprint model of a user based on a security code speech signal sent from the client, wherein the security code speech signal is generated when the requester reads out the dynamic security code;
• judging the identity of the requester based on the calculated comprehensive confidence of the identity;
• and feeding an identity authentication result back to the client,
• wherein
• the dynamic security code speech-based identity authentication system is provided with an automatic reconstruction subsystem for the voiceprint model, and the voiceprint model of the user is reconstructed by the automatic reconstruction subsystem for the voiceprint model when the identity authentication result is that the requester is the user of the server, and
• the automatic reconstruction subsystem for the voiceprint model comprises:
• a time-varying data storage unit for storing speech data of each user with time labels;
• a time-varying data updating module for storing the security code speech signal as a latest speech data into the time-varying data storage unit;
• a time window channel construction module for extracting the speech data of the user from the time-varying data storage unit in an order of the time labels, constructing a time window channel including a plurality of sets of speech data, and updating the speech data included in the time window channel using the latest speech data;
• and
• a voiceprint model reconstruction module for reconstructing the voiceprint model of the user for the user using the plurality of sets of speech data included in the updated time window channel,
• wherein,
• the automatic reconstruction subsystem for the voiceprint model further comprises a parameterization module for speech data, and the parameterization module for speech data is used for parameterizing the security code speech signal, i.e., speech data, to obtain a latest parameterized speech data;
• parameterized speech data of each user is stored with time labels in the time-varying data storage unit;
• the latest parameterized speech data is stored in the time-varying data storage unit by the time-varying data updating module;
• the time window channel construction module extracts parameterized speech data of the user from the time-varying data storage unit in the order of the time labels, constructs
• a time window channel including a plurality of sets of parameterized speech data, and updates the parameterized speech data included in the time window channel using the
• latest parameterized speech data;
• and
• the voiceprint model reconstruction module reconstructs the voiceprint model of the user for the user using the plurality of sets of parameterized speech data included in the updated time window channel,
• the automatic reconstruction subsystem for the voiceprint model further comprises a speech recognition module for recognizing phonemes corresponding to respective frames in the speech data;
• phonemes corresponding to the latest parameterized speech data and frame intervals corresponding to the phonemes are further stored in the time-varying data storage unit;
• and
• the time window channel construction module updates the parameterized speech data included in the time window channel based on the phonemes corresponding to the latest parameterized speech data, so that phonemes corresponding to the plurality of sets of parameterized speech data included in the time window channel are evenly distributed, and,
• the time window channel construction module tentatively removes a set of parameterized speech data from the time window channel sequentially in the order of the time labels from old to new, and calculates an equilibrium degree of a character-based phoneme distribution based on all of parameterized speech data remained in the time window channel and the latest parameterized speech data, and if the equilibrium degree is greater than or equal to a predetermined threshold of the equilibrium degree, the latest parameterized speech data is pushed into the time window channel;
• otherwise, the set of parameterized speech data tentatively removed is restored to the time window channel, and then a next set of parameterized speech data is tentatively removed from the time window channel, and once again, the time window channel construction module calculates the equilibrium degree based on all of parameterized speech data remained in the time window channel and the latest parameterized speech data till each set of parameterized speech data included in the time window channel has been tentatively removed or the latest parameterized speech data has been pushed into the time window channel.