Abstract: The learning data generation device (10) of the present invention comprises: an end-of-talk predict unit (11) for performing: a first prediction in which it is predicted, based on utterance information on an utterance in the dialog, using the end-of-talk prediction model (16), whether the utterance is an end-of-talk utterance of the speaker; and a second prediction in which it is predicted, based on one or more prescribed rules, whether the utterance is an end-of-talk utterance; and a training data generate unit (13) for generating, when, in the first prediction it is predicted that the utterance is not an end-of-talk utterance and in the second prediction it is predicted that the utterance is an end-of-talk utterance, for the utterance information on the utterance, learning data to which training data indicating that the utterance is an end-of-talk utterance is appended.

Abstract: A technique is provided that can reduce degradation of the sound quality due to a tandem connection of paired coding and decoding, and can reduce the operation processing amount and the required memory amount of a multipoint control unit. In multipoint connection between terminals of a plurality of communication networks (for example, a fixed phone line and a mobile phone line) having different communication capacities, when a multichannel coding including a monaural coding scheme of a communication network having a smaller communication capacity is used in a communication network having a larger communication capacity to transmit sounds of a plurality points to the communication network terminal having the larger communication capacity, control is exercised such that monaural codes of the plurality points are output.

Abstract: A computer-implemented method for analyzing call interactions in an interactions database by a Proactive Fraud Exposure (PFE) engine is provided herein. The computer-implemented method may generate a voiceprint for each call interaction; (ii) use a machine learning technique to group the call interactions into one or more clusters based on respective voiceprints in the voiceprints database; (iii) store the one or more clusters; and (iv) rank and classifying the one or more clusters to yield a list of potential fraudsters. The computer-implemented method may further transmit the list of potential fraudsters to a user to enable the user to review said list of potential fraudsters and to add fraudsters from the list to a watchlist database.