Abstract: A system and method for generating an agent behavioral analytics including transcribing an incoming call to produce a call transcription; and using a trained convolutional neural network (CNN) to produce behavioral labels for the agent in the incoming call for behavioral metrics, based on the call transcription. The CNN may include an embedding layer to convert words in the call transcription into vectors in a word embedding space; a convolution layer to perform a plurality of convolutions on the vectors and to generate vectors of features; a pooling layer to concatenate the vectors of features to a single vector by taking a maximum of each feature generated by the convolution layer; and a classification layer to produce grades of the agent in the incoming call for the set of attributes or behavioral metrics, based on the single vector generated by the pooling layer.

Abstract: A system and method for generating an agent behavioral analytics including extracting text-based features, sentiment-based features, and prosody-based features from tagged calls; training a machine learning behavioral model, based on the text-based features, the sentiment-based features, and the prosody-based features extracted from the tagged calls and an at least one score associated with an at least one behavioral metric of the tagged calls, to produce a trained machine learning behavioral model; extracting text-based features, sentiment-based features, and prosody-based features from an incoming call; and using the trained machine learning behavioral model to produce an at least one behavioral label for the agent in the incoming call for the at least one behavioral metric, based on the text-based features of the incoming call, the sentiment-based features of the incoming call and the prosody-based features of the incoming call.

Abstract: A system and method for generating an agent behavioral analytics including transcribing an incoming call to produce a call transcription; and using a trained convolutional neural network (CNN) to produce behavioral labels for the agent in the incoming call for behavioral metrics, based on the call transcription. The CNN may include an embedding layer to convert words in the call transcription into vectors in a word embedding space; a convolution layer to perform a plurality of convolutions on the vectors and to generate vectors of features; a pooling layer to concatenate the vectors of features to a single vector by taking a maximum of each feature generated by the convolution layer; and a classification layer to produce grades of the agent in the incoming call for the set of attributes or behavioral metrics, based on the single vector generated by the pooling layer.

Abstract: A system and method for generating an agent behavioral analytics including extracting text-based features, sentiment-based features, and prosody-based features from tagged calls; training a machine learning behavioral model, based on the text-based features, the sentiment-based features, and the prosody-based features extracted from the tagged calls and an at least one score associated with an at least one behavioral metric of the tagged calls, to produce a trained machine learning behavioral model; extracting text-based features, sentiment-based features, and prosody-based features from an incoming call; and using the trained machine learning behavioral model to produce an at least one behavioral label for the agent in the incoming call for the at least one behavioral metric, based on the text-based features of the incoming call, the sentiment-based features of the incoming call and the prosody-based features of the incoming call.