System and method for filtering reproduced communication in real-time

A method of filtering reproduced communication in real-time, the method comprising:

• determining, by a processor and in real-time, a plurality of data vectors associated with a communication input received via a user device;
• determining, by the processor, a set of spectrograms corresponding to a first set of data vectors from the plurality of data vectors;
• determining, by the processor, a fused vector from a second set of data vectors from the plurality of data vectors using a vector fusing technique;
• determining, by the processor, a first confidence score based on an analysis of the set of spectrograms using a vision transformer model;
• determining, by the processor, a second confidence score based on an analysis of the fused vector using a deep learning classification model;
• determining, by the processor, a weighted confidence score based on the first confidence score, the second confidence score and corresponding predefined weights associated to the first confidence score and the second confidence score;
• classifying, by the processor, the communication input as one of a plurality of communication types based on the weighted confidence score and a set of predefined thresholds;
• and
• filtering, by the processor, the communication input received by the user device based on the classification.

A non-transitory computer-readable medium storing computer-executable instructions for filtering reproduced communication in real-time, the computer-executable instructions configured for:

• determining, in real-time, a plurality of data vectors associated with a communication input received via a user device;
• determining a set of spectrograms corresponding to a first set of data vectors from the plurality of data vectors;
• determining a fused vector from a second set of data vectors from the plurality of data vectors using a vector fusing technique;
• determining a first confidence score based on an analysis of the set of spectrograms using a vision transformer model;
• determining a second confidence score based on an analysis of the fused vector using a deep learning classification model;
• determining a weighted confidence score based on the first confidence score, the second confidence score and corresponding predefined weights associated to the first confidence score and the second confidence score;
• classifying the communication input as one of a plurality of communication types based on the weighted confidence score and a set of predefined thresholds;
• and
• filtering the communication input received by the user device based on the classification.