Abstract: A method, computer program product, and computing system for disentangling background information from speaker information in a speech signal. Background information is extracted from the speech signal to generate a background acoustics embedding and speaker information is extracted from the speech signal to generate a speaker acoustics embedding. A first loss factor is applied to the background acoustics embedding to decrease speaker information therein to generate a processed background acoustics embedding using machine learning and a second loss factor is applied to the speaker acoustics embedding to decrease background information therein to generate a processed speaker acoustics embedding using machine learning. At least one of the processed background acoustics embedding and the processed speaker acoustics embedding is output to a speech processing system.

Abstract: A multi-modal artificial neural network and a self-supervised learning method for training that network. The learning method involves processing, using a first modality simple Siamese network, a pair of first modality augmented views of an input; processing, using a second modality simple Siamese network, a pair of second modality augmented views of the input; determining at least one cross-modal loss between the first and second modality simple Siamese networks; determining a total loss from: (i) first and second modality losses respectively determined during the processing using the first and second modality simple Siamese networks; and (ii) the at least one cross-modal loss; and training the first and second modality simple Siamese networks based on the total loss. The trained network may be used to analyze multi-modal content such as video content that has an audio track. A Multi-Modal Multi-Head Network (M3HN) may also be trained to process modality-specific and modality-agnostic representations.

Abstract: The disclosure presents herein a method to train a classifier in a machine learning using more than one simultaneous sample to address class imbalance problem in any discriminative classifier. A modified representation of the training dataset is obtained by simultaneously considering features based representations of more than one sample. A modification to an architecture of a classifier is needed into handling the modified date representation of the more than one samples. The modification of the classifier directs same number of units in the input layer as to accept the plurality of simultaneous samples in the training dataset. The output layer will consist of units equal to twice the considered number of classes in the classification task, therefore, the output layer herein will have four units for two-class classification task. The disclosure herein can be implemented to resolve the problem of learning from low resourced data.

Abstract: A system and method for monitoring behavior of voice agents in a simulated environment of voice-based call center to route a call. It includes a set of models and wearable devices to estimate and analyze cognitive load and emotional state of a voice agent which are obtained using wearable devices in the real time. It collects physiological signals from the voice agents and analyze them along with skill-set profiles of the voice agent to identify best suited voice agent based on agent-customer matching score obtained using skill-set profile analysis, cognitive load and a predicted emotive state of the voice agent. It may assist the voice agent in call if the cognitive load of the voice agent raises beyond predefined threshold using brain computer interfacing.

Abstract: A system and method for monitoring behavior of voice agents in a simulated environment of voice-based call center to route a call. It includes a set of models and wearable devices to estimate and analyze cognitive load and emotional state of a voice agent which are obtained using wearable devices in the real time. It collects physiological signals from the voice agents and analyze them along with skill-set profiles of the voice agent to identify best suited voice agent based on agent-customer matching score obtained using skill-set profile analysis, cognitive load and a predicted emotive state of the voice agent. It may assist the voice agent in call if the cognitive load of the voice agent raises beyond predefined threshold using brain computer interfacing.

Abstract: The disclosure presents herein a method to train a classifier in a machine learning using more than one simultaneous sample to address class imbalance problem in any discriminative classifier. A modified representation of the training dataset is obtained by simultaneously considering features based representations of more than one sample. A modification to an architecture of a classifier is needed into handling the modified date representation of the more than one samples. The modification of the classifier directs same number of units in the input layer as to accept the plurality of simultaneous samples in the training dataset. The output layer will consist of units equal to twice the considered number of classes in the classification task, therefore, the output layer herein will have four units for two-class classification task. The disclosure herein can be implemented to resolve the problem of learning from low resourced data.