Frequency mapping in the voiceprint domain

A system comprising:

• a processor;
• and
• a memory storing programming instructions for execution by the processor, wherein the programming instructions, upon execution by the processor, cause the processor to perform the following operations:
• training, during a training process, a neural net mapping model of a wideband (WB) speaker recognition system to perform narrowband-to-wideband (NB-to-WB) voice vector transformation exclusively using a WB training dataset that excludes known narrowband (NB) voice vectors associated with known speaker identities, the WB training dataset being composed of WB audio samples produced by sampling utterances of people at a WB sampling frequency,
• wherein training the neural net mapping model comprises;
• processing one of the WB audio samples from the WB training dataset via a WB processing path to obtain a target WB voice vector:
• processing the WB audio sample via a NB processing path, that is independent from the WB processing path, to obtain an input NB voice vector;
• and
• regressively training the neural net mapping model to transform the input NB voice vector into an approximated WB voice vector that reduces distortion between the approximated WB voice vector and the target WB voice vector,
• wherein the neural net mapping model is regressively trained using a loss function measuring the distortion between the approximated WB voice vector and the target WB voice vector;
• and
• after the training process has concluded, receiving an input WB voice vector of a speaker and performing, by the WB speaker recognition system based on the known NB voice vectors, WB speaker identification on the input WB voice vector using the trained neural net mapping model, the input WB voice vector produced by a user device directly sampling human voice audio of the speaker at the WB sampling frequency,
• wherein performing WB speaker identification on the input WB voice vector includes:
• mapping the input WB voice vector to one of the known NB voice vectors;
• using the trained neural net mapping model to transform the known NB voice vector into a representative WB voice vector:
• comparing the input WB voice vector with the representative WB voice vector to determine a score that indicates a probability that the input WB voice vector and the known NB voice vector originated from a same person;
• and
• identifying the speaker based on a corresponding one of the known speaker identities associated with the known NB voice vector when the score exceeds a second threshold,
• wherein the trained neural net mapping model transforms the known NB voice vector into the representative WB voice vector during a time period between reception of the input WB voice vector and identification of the speaker by the WB speaker recognition system.

A storage device that is non-transitory, comprising instructions that are readable by a processor to cause said processor to perform operations of:

• training, during a training process, a neural net mapping model of a wideband (WB) speaker recognition system to perform narrowband-to-wideband (NB-to-WB) voice vector transformation exclusively using a WB training dataset that excludes known narrowband (NB) voice vectors associated with known speaker identities, the WB training dataset being composed of WB audio samples produced by sampling utterances of people at a WB sampling frequency,
• wherein training the neural net mapping model comprises;
• processing one of the WB audio samples from the WB training dataset via a WB processing path to obtain a target WB voice vector;
• processing the WB audio sample via a NB processing path, that is independent from the WB processing path, to obtain an input NB voice vector, and
• regressively training the neural net mapping model to transform the input NB voice vector into an approximated WB voice vector that reduces distortion between the approximated WB voice vector and the target WB voice vector,
• wherein the neural net mapping model is regressively trained using a loss function measuring the distortion between the approximated WB voice vector and the target WB voice vector;
• and
• after the training process has concluded, receiving an input WB voice vector of a speaker and performing, by the WB speaker recognition system based on the known NB voice vectors, WB speaker identification on the input WB voice vector using the trained neural net mapping model, the input WB voice vector produced by a user device directly sampling human voice audio of the speaker at the WB sampling frequency,
• wherein performing WB speaker identification on the input WB voice vector includes:
• mapping the input WB voice vector to one of the known NB voice vectors;
• using the trained neural net mapping model to transform the known NB voice vector into a representative WB voice vector;
• comparing the input WB voice vector with the representative WB voice vector to determine a score that indicates a probability that the input WB voice vector and the known NB voice vector originated from a same person;
• and
• identifying the speaker based on a corresponding one of the known speaker identities associated with the known NB voice vector when the score exceeds a second threshold,
• wherein the trained neural net mapping model transforms the known NB voice vector into the representative WB voice vector during a time period between reception of the input WB voice vector and identification of the speaker by the WB speaker recognition system.