Speaker recognition with quality indicators

A system comprising:

• a database configured store an enrolled voiceprint for an enrolled speaker;
• and
• a server comprising a processor configured to:
• extract from an inbound audio signal for an inbound speaker a feature vector for one or more acoustic features;
• generate one or more quality measures and an overall quality measure for the inbound audio signal, by applying a first machine-learning architecture to the feature vector for the one or more acoustic features, the one or more quality measures corresponding to a similarity between one or more expected quality descriptors and one or more quality descriptors for the call audio of the inbound audio signal;
• extract an inbound speaker embedding for the inbound speaker from the one or more acoustic features for the inbound audio signal, by applying a second machine-learning architecture to the feature vector for the one or more acoustic features of the inbound audio signal;
• generate a first similarity score for the inbound speaker based upon the inbound speaker embedding and the enrolled voiceprint for the enrolled speaker, by applying the second machine-learning architecture;
• generate a second similarity score for verifying the inbound speaker, the second similarity score generated based upon the one or more quality measures and the first similarity score;
• and
• verify the inbound speaker as the enrolled speaker based upon comparing the second similarity score against a verification threshold.

A non-transitory computer-readable medium comprising a non-transitory storage memory configured to store machine-readable instructions that when executed by a processor instruct the processor to:

• extract from an inbound audio signal for an inbound speaker a feature vector for one or more acoustic features;
• generate one or more quality measures and an overall quality measure for the inbound audio signal, by applying a first machine-learning architecture to the feature vector for the one or more acoustic features;
• extract an inbound speaker embedding for the inbound speaker from the one or more acoustic features for the inbound audio signal, by applying a second machine-learning architecture to the feature vector for the one or more acoustic features of the inbound audio signal, the one or more quality measures corresponding to a similarity between one or more expected quality descriptors and one or more quality descriptors for the call audio of the inbound audio signal;
• generate a first similarity score for the inbound speaker based upon the inbound speaker embedding and an enrolled voiceprint for an enrolled speaker, by applying the second machine-learning architecture;
• generate a second similarity score for verifying the inbound speaker, the second similarity score generated based upon the one or more quality measures and the first similarity score;
• and
• verify the inbound speaker as the enrolled speaker based upon comparing the second similarity score against a verification threshold.