NICE Systems

About: NICE Systems is a based out in . It is known for research contribution in the topics: Session (computer science) & Audio signal. The organization has 277 authors who have published 197 publications receiving 5264 citations.

Methods and apparatus for language identification

Abstract: In a multi-lingual environment, a method and apparatus for determining a language spoken in a speech utterance. The method and apparatus test acoustic feature vectors extracted from the utterances against acoustic models associated with one or more of the languages. Speech to text is then performed for the language indicated by the acoustic testing, followed by textual verification of the resulting text. During verification, the resulting text is processed by language specific NLP and verified against textual models associated with the language. The system is self-learning, i.e., once a language is verified or rejected, the relevant feature vectors are used for enhancing one or more acoustic models associated with one or more languages, so that acoustic determination may improve.

Adaptive, self-learning optimization module for rule-based customer interaction systems

Abstract: The invention relates to a call center system having automatic means for optimizing those rules that are enforced over interactions with customers, said system comprises: (a) an infrastructure which comprises a CRM module for at least handling the interactions with customers, and hardware means for at least maintaining the communication with said customers; (b) management rules, each rule comprising one or more rule parameters, said rules parameters enforcing interaction behavior during all interactions with customers; and (c) an adaptive, self learning module, for: (c.1) monitoring all interactions with customers; (c.2) upon completion of each interaction, recording a corresponding set of full interaction details, said set of full interaction details includes those rule parameters that were enforced during said interaction, and those additional interaction parameters that are specific to that interaction; and (c.3) using an adaptive engine, periodically analyzing one or more of said sets of recorded full interaction details, and producing one or more modified rules having modified rule parameters, and enforcing said modified rules over future interactions with customers.

System and method for developing and testing logic in a mock-up environment

Abstract: A system and method is provided for generating logic to automate target applications. The logic may be programmed in a virtual environment using mock-up screen elements that mimic the behavior of real screen elements in the target applications environments. The programmed logic may be executed in a computer system using the real screen elements in the target applications environments. The operating environment may be switched between the virtual environment in a mock-up mode and the target applications environment in a real mode.

Language model adaptation for specific texts

Abstract: A computerized method for adapting a baseline language model, comprising obtaining a textual corpus of documents that comprise textual expressions, incorporating in the baseline language model textual expressions from documents which are determined as relevant to a provided target text based on a plurality of different relevancy determinations between the documents and the provided target text, thereby adapting the baseline language model to form an adapted language model for recognizing terms of a context of the provided target text, wherein the method is automatically performed on an at least one computerized apparatus configured to perform the method.

System and method for distributed categorization

Abstract: A generic categorization method may include receiving interaction data on a distributed computing system operating on a plurality of computing nodes. The distributed computing system may distribute the received interaction data across the plurality of nodes. On each node, categorization rules may be applied to the interaction data via parallel processing. The results, which may include a category associated with each interaction, may be written to a distributed storage system. A user interface may allow a user to define the categorization rules and schemas of interaction data.