Abstract:
A method for identifying concepts in a plurality of interactions includes: filtering, on a processor, the interactions based on intervals; creating, on the processor, a plurality of sentences from the filtered interactions; computing, on the processor, a saliency of each the sentences; pruning away, on the processor, sentences with low saliency for generating a set of informative sentences; clustering, on the processor, the sentences of the set of informative sentences for generating a plurality of sentence clusters, each of the clusters corresponding to a concept of the concepts; computing, on the processor, a saliency of each of the clusters; and naming, on the processor, each of the clusters.
Abstract:
A method for detecting and categorizing topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; filtering, by the processor, the plurality of fragments to generate a filtered plurality of fragments; clustering, by the processor, the filtered fragments into a plurality of base clusters; and clustering, by the processor, the plurality of base clusters into a plurality of hyper clusters.
Abstract:
A method for automatically calculating an overall evaluation score of an interaction includes: receiving, by a processor, an evaluation form, the evaluation form comprising a plurality of automatic questions and a plurality of manual questions; automatically extracting, by a processor, a set of features from the interaction, the set of features comprising answers to the automatic questions without manually generated answers to the manual questions; and computing an overall evaluation score based on the set of features.
Abstract:
A method for detecting and categorizing topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; filtering, by the processor, the plurality of fragments to generate a filtered plurality of fragments; clustering, by the processor, the filtered fragments into a plurality of base clusters; and clustering, by the processor, the plurality of base clusters into a plurality of hyper clusters.
Abstract:
A method for extracting, from non-speech text, training data for a language model for speech recognition includes: receiving, by a processor, non-speech text; selecting, by the processor, text from the non-speech text; converting, by the processor, the selected text to generate converted text comprising a plurality of phrases consistent with speech transcription text; training, by the processor, a language model using the converted text; and outputting, by the processor, the language model.
Abstract:
A method for predicting a speech recognition quality of a phrase comprising at least one word includes: receiving, on a computer system including a processor and memory storing instructions, the phrase; computing, on the computer system, a set of features comprising one or more features corresponding to the phrase; providing the phrase to a prediction model on the computer system and receiving a predicted recognition quality value based on the set of features; and returning the predicted recognition quality value.
Abstract:
A method for generating a dialogue tree for an automated self-help system of a contact center from a plurality of recorded interactions between customers and agents of the contact center includes: computing, by a processor, a plurality of feature vectors, each feature vector corresponding to one of the recorded interactions; computing, by the processor, similarities between pairs of the feature vectors; grouping, by the processor, similar feature vectors based on the computed similarities into groups of interactions; rating, by the processor, feature vectors within each group of interactions based on one or more criteria, wherein the criteria include at least one of interaction time, success rate, and customer satisfaction; and outputting, by the processor, a dialogue tree in accordance with the rated feature vectors for configuring the automated self-help system.
Abstract:
A method for configuring an automated self-help system based on prior interactions between a plurality of customers and a plurality of agents of a contact center includes: recognizing, by a processor, speech in the prior interactions between customers and agents to generate recognized text, the recognized text including a plurality of phrases, the phrases being classified into a plurality of clusters; extracting, by the processor, a plurality of sequences of clusters, each of the sequences of clusters corresponding to the phrases of one of the prior interactions; filtering, by the processor, the sequences of clusters based on a criterion; mining, by the processor, a preliminary dialogue tree from the sequences of clusters; invoking configuration of the automated self-help system based on the preliminary dialogue tree; and outputting a dialogue tree for configuring the automated self-help system.
Abstract:
A method for configuring an automated, speech driven self-help system based on prior interactions between a plurality of customers and a plurality of agents includes: recognizing, by a processor, speech in the prior interactions between customers and agents to generate recognized text; detecting, by the processor, a plurality of phrases in the recognized text; clustering, by the processor, the plurality of phrases into a plurality of clusters; generating, by the processor, a plurality of grammars describing corresponding ones of the clusters; outputting, by the processor, the plurality of grammars; and invoking configuration of the automated self-help system based on the plurality of grammars.