International Business Machines Corporation

Weizhong Zhu

Kenneth W Church

Jason William Pelecanos

Martin Franz

Dimitrios B Dimitriadis

Jeffrey Scott McCarley

Petr Fousek

Nicholas Stephen Kersting

Josef Vopicka

A diarization embodiment may include a system that clusters data up to a current point in time and consolidates it with the past decisions, and then returns the result that minimizes the difference with past decisions. The consolidation may be achieved by performing a permutation of the different possible labels and comparing the distance. For speaker diarization, a distance may be determined based on a minimum edit or hamming distance. The distance may alternatively be a measure other than the minimum edit or hamming distance. The clustering may have a finite time window over which the analysis is performed.

Prefix methods for diarization in streaming mode

Embodiments of the present invention provide a computer-implemented method for communicating a reference code for a transaction. The method monitors a communication session conducted between a user and an agent via a communication channel, extracts user and channel properties from the monitored communication session, selects a reference code from a set of references codes stored on a database, in which the selection is based at least in part on the extracted communication channel properties and the extracted user properties, and then communicates the selected reference code to the user.

Adaptive selection of message data properties for improving communication throughput and reliability

Embodiments of the invention include method, systems and computer program products for role modeling. Aspects of the invention include receiving, by a processor, audio data, wherein the audio data includes a plurality of audio conversation for one or more speakers. The one or more segments for each of the plurality of audio conversations are partitioned. A speaker is associated with each of the one or more segments. The one or more segments for each of the plurality of audio conversations are labeled with roles utilizing a speaker recognition engine. Speakers are clustered based at least in part on a number of times the speakers are present in an audio conversation.

Role modeling in call centers and work centers

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Hartsdale, NY, United States of America

Weizhong Zhu