Monitoring, mining, and classifying electronically recordable conversations
First Claim
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1. A computer-readable storage medium having computer-executable instructions for performing steps comprising:
- generating a feature that is based on speech signals from at least two participants in a conversation held over an electronically recordable channel, the feature comprising a feature from a group of features consisting of;
speaking rate of each participant, energy of speech of each participant, amount of overlapping speech, number of times a different participant begins to speak, number of interruptions, identity of dominant participant, and length of pauses in the speech of the participants;
identifying a deviation between the feature and a conversation model;
applying the deviation to a plurality of deviation models to identify which model best matches the identified deviation; and
classifying the conversation into one of a number of categories based on a label associated with the deviation model that best matches the identified deviation.
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Abstract
Conversations that take place over an electronically recordable channel are analyzed by constructing a set of features from the speech of two participants in the conversation. The set of features is applied to a model or a plurality of models to determine the likelihood of the set of features for each model. These likelihoods are then used to classify the conversation into categories, provide real-time monitoring of the conversation, and/or identify anomalous conversations.
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Citations
5 Claims
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1. A computer-readable storage medium having computer-executable instructions for performing steps comprising:
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generating a feature that is based on speech signals from at least two participants in a conversation held over an electronically recordable channel, the feature comprising a feature from a group of features consisting of;
speaking rate of each participant, energy of speech of each participant, amount of overlapping speech, number of times a different participant begins to speak, number of interruptions, identity of dominant participant, and length of pauses in the speech of the participants;identifying a deviation between the feature and a conversation model; applying the deviation to a plurality of deviation models to identify which model best matches the identified deviation; and classifying the conversation into one of a number of categories based on a label associated with the deviation model that best matches the identified deviation. - View Dependent Claims (2, 3, 4, 5)
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Specification