Systems and methods for detecting emotion from audio files

A system comprising:

• one or more processors;
• and
• a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:
• receive a dual-channel audio file comprising an interaction between a first user and a second user;
• detect, using a neural network, one or more moments of interruption between the first and second users from the dual-channel audio file;
• extract, using the neural network, one or more vocal features associated with each of the one or more moments of interruption;
• determine, based on the one or more vocal features, whether a threshold number of moments of the one or more moments of interruption corresponds to a first emotion type;
• when the threshold number of moments corresponds to the first emotion type, transmit a first message comprising a first binary indication;
• and
• when the threshold number of moments does not correspond to the first emotion type, transmit a second message comprising a second binary indication.

A system comprising:

• one or more processors;
• and
• a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to:
• receive an audio file comprising an interaction between a first user and a second user;
• detect, using a first machine learning model, one or more moments of interruption between the first and second users from the audio file;
• when a threshold number of one or more moments of interruption corresponds to a first emotion type, classify the audio file as associated with user agitation;
• and
• when the threshold number of one or more moments of interruption does not correspond the first emotion type, classify the audio file as associated with user non-agitation.