Abstract: Techniques are disclosed for recognizing user personality in accordance with a speech recognition system. For example, a technique for recognizing a personality trait associated with a user interacting with a speech recognition system includes the following steps/operations. One or more decoded spoken utterances of the user are obtained. The one or more decoded spoken utterances are generated by the speech recognition system. The one or more decoded spoken utterances are analyzed to determine one or more linguistic attributes (morphological and syntactic filters) that are associated with the one or more decoded spoken utterances. The personality trait associated with the user is then determined based on the analyzing step/operation.

Abstract: The present invention addresses the deficiencies in the prior art by providing an improved dialog for disambiguating a user utterance containing more than one intent. The invention comprises methods, computer-readable media, and systems for engaging in a dialog. The method embodiment of the invention relates to a method of disambiguating a user utterance containing at least two user intents. The method comprises establishing a confidence threshold for spoken language understanding to encourage that multiple intents are returned, determining whether a received utterance comprises a first intent and a second intent and, if the received utterance contains the first intent and the second intent, disambiguating the first intent and the second intent by presenting a disambiguation sub-dialog wherein the user is offered a choice of which intent to process first, wherein the user is first presented with the intent of the first or second intents having the lowest confidence score.

Abstract: The present invention addresses the deficiencies in the prior art by providing an improved dialog for disambiguating a user utterance containing more than one intent. The invention comprises methods, computer-readable media, and systems for engaging in a dialog. The method embodiment of the invention relates to a method of disambiguating a user utterance containing at least two user intents. The method comprises establishing a confidence threshold for spoken language understanding to encourage that multiple intents are returned, determining whether a received utterance comprises a first intent and a second intent and, if the received utterance contains the first intent and the second intent, disambiguating the first intent and the second intent by presenting a disambiguation sub-dialog wherein the user is offered a choice of which intent to process first, wherein the user is first presented with the intent of the first or second intents having the lowest confidence score.

Abstract: The present invention addresses the deficiencies in the prior art by providing an improved dialog for disambiguating a user utterance containing more than one intent. The invention comprises methods, computer-readable media, and systems for engaging in a dialog. The method embodiment of the invention relates to a method of disambiguating a user utterance containing at least two user intents. The method comprises establishing a confidence threshold for spoken language understanding to encourage that multiple intents are returned, determining whether a received utterance comprises a first intent and a second intent and, if the received utterance contains the first intent and the second intent, disambiguating the first intent and the second intent by presenting a disambiguation sub-dialog wherein the user is offered a choice of which intent to process first, wherein the user is first presented with the intent of the first or second intents having the lowest confidence score.

Abstract: Techniques are disclosed for recognizing user personality in accordance with a speech recognition system. For example, a technique for recognizing a personality trait associated with a user interacting with a speech recognition system includes the following steps/operations. One or more decoded spoken utterances of the user are obtained. The one or more decoded spoken utterances are generated by the speech recognition system. The one or more decoded spoken utterances are analyzed to determine one or more linguistic attributes (morphological and syntactic filters) that are associated with the one or more decoded spoken utterances. The personality trait associated with the user is then determined based on the analyzing step/operation.

Abstract: Techniques are disclosed for recognizing user personality in accordance with a speech recognition system. For example, a technique for recognizing a personality trait associated with a user interacting with a speech recognition system includes the following steps/operations. One or more decoded spoken utterances of the user are obtained. The one or more decoded spoken utterances are generated by the speech recognition system. The one or more decoded spoken utterances are analyzed to determine one or more linguistic attributes (morphological and syntactic filters) that are associated with the one or more decoded spoken utterances. The personality trait associated with the user is then determined based on the analyzing step/operation.

Abstract: A method of processing limited natural language data to automatically develop an optimal feature set, bypassing the standard Wizard of OZ (WOZ) approach. Natural language understanding models process existing data from other domains, such as the Internet, for domain-specific adaptation through the use of an optimal feature set. When the optimal feature set is passed on to any engine, the optimal feature set produces robust models that can be used for natural language call routing.

Abstract: Techniques for recognizing a personality trait associated with a user. Input from the user is analyzed to determine a number of words, including a number of compound words. The personality trait associated with the user is determined based, at least in part, on the number of compound words exceeding a threshold.

Abstract: Systems ana methods are provided for organizing and presenting large search results lists using lexical semantic componential-gradient processing techniques to dynamically organize search results into a gradable list of context rich semantic components, which is presented to a user under a gradient as a constrained set of choices to thereby facilitate user navigation of the search results.

Abstract: A method of processing limited natural language data to automatically develop an optimal feature set, bypassing the standard Wizard of OZ (WOZ) approach is provided. The method provides for building natural language understanding models or for processing existing data from other domains, such as the Internet, for domain-specific adaptation through the use of an optimal feature set. Consequently, when the optimal feature set is passed on to any engine, the optimal feature set produces robust models that can be used for natural language call routing.

Abstract: A method of processing limited natural language data to automatically develop an optimal feature set, bypassing the standard Wizard of OZ (WOZ) approach is provided. The method provides for building natural language understanding models or for processing existing data from other domains, such as the Internet, for domain-specific adaptation through the use of an optimal feature set. Consequently, when the optimal feature set is passed on to any engine, the optimal feature set produces robust models that can be used for natural language call routing.

Abstract: Techniques are disclosed for recognizing user personality in accordance with a speech recognition system. For example, a technique for recognizing a personality trait associated with a user interacting with a speech recognition system includes the following steps/operations. One or more decoded spoken utterances of the user are obtained. The one or more decoded spoken utterances are generated by the speech recognition system. The one or more decoded spoken utterances are analyzed to determine one or more linguistic attributes (morphological and syntactic filters) that are associated with the one or more decoded spoken utterances. The personality trait associated with the user is then determined based on the analyzing step/operation.