Abstract: Automated systems and methods are provided for processing speech, comprising obtaining a digitally-encoded speech representation corresponding to a telecommunication interaction, wherein the digitally-encoded speech representation includes at least one of a voice recording or a transcript derived from audio of the telecommunication interaction; obtaining a digitally-encoded data set corresponding to at least one structured feature of the telecommunication interaction; obtaining a reference set, wherein the reference set includes a set of binary-classified existing satisfaction classifications; obtaining a trained machine learning algorithm, wherein the machine learning algorithm has been trained using a first plurality of reference telecommunication interactions which include user-provided satisfaction scores; extracting a feature set from the digitally-encoded speech representation; and by the machine learning algorithm and based on at least one structured feature and the feature set, generating a predicted

Abstract: Automated systems and methods are provided for processing speech, comprising obtaining a trained machine learning model that has been trained using a cumulative historical data structure corresponding to at least one digitally-encoded speech representation for a plurality of telecommunications interactions conducted by a plurality of agent-side participants, which includes a first data corresponding to a score variable and a second data corresponding to a plurality of driver variables; applying the trained machine learning model: to a subset of data in the cumulative historical data structure that corresponds to a first agent-side participant of the plurality of agent-side participants, to generate a performance classification score and/or a performance direction classification score, to identify an intervention-target agent-side participant from among the plurality of agent-side participants, and to the cumulative historical data structure to identify an intervention training plan; and conducting at least on

Abstract: Automated systems and methods are provided for processing speech, comprising obtaining a trained machine learning model that has been trained using a cumulative historical data structure corresponding to at least one digitally-encoded speech representation for a plurality of telecommunications interactions conducted by a plurality of agent-side participants, which includes a first data corresponding to a score variable and a second data corresponding to a plurality of driver variables; applying the trained machine learning model: to a subset of data in the cumulative historical data structure that corresponds to a first agent-side participant of the plurality of agent-side participants, to generate a performance classification score and/or a performance direction classification score, to identify an intervention-target agent-side participant from among the plurality of agent-side participants, and to the cumulative historical data structure to identify an intervention training plan; and conducting at least on

Abstract: Automated systems and methods are provided for processing speech, comprising obtaining a digitally-encoded speech representation corresponding to a telecommunication interaction, wherein the digitally-encoded speech representation includes at least one of a voice recording or a transcript derived from audio of the telecommunication interaction; obtaining a digitally-encoded data set corresponding to at least one structured feature of the telecommunication interaction; obtaining a reference set, wherein the reference set includes a set of binary-classified existing satisfaction classifications; obtaining a trained machine learning algorithm, wherein the machine learning algorithm has been trained using a first plurality of reference telecommunication interactions which include user-provided satisfaction scores; extracting a feature set from the digitally-encoded speech representation; and by the machine learning algorithm and based on at least one structured feature and the feature set, generating a predicted