Abstract: Techniques for predicting user interests based on information known about a specific context is provided. A context-independent relevance function is generated from information gathered from many users and/or from many documents (or files). Information about a specific context (e.g., a particular user, a particular group of users, or type of content) is used to adapt the CI relevance function to the specific context. Based on a query submitted by a user, the adapted relevance function is used to identify results that the user would most likely be interested in. Results may include references to webpages and advertisements.

Abstract: Mechanisms model, detect, and predict user behavior as a user navigates the Web. In one embodiment, mechanisms model user behavior using predictive models, such as discrete Markov processes, where the user's behavior transitions between a finite number of states. The user's behavior state may not be directly observable (e.g., a user does not proactively indicate what behavior state he is in). Thus, the behavior state of a user is usually only indirectly observable. Mechanisms use predictive models, such as hidden Markov models, to predict the transitions in the user's behavior states.

Abstract: A QA robot learns how to answer questions by observing human interaction over online social networks. The QA robot observes the way people ask questions and how other users respond to those questions. In addition, the QA robot observes which questions are most helpful and analyzes those questions to identify the characteristics of those questions that are most helpful. The QA robot then uses those observations to enhance the way it answers questions in the future.

Abstract: A system which uses automatic speech recognition to provide dialogs with human speakers automatically detects one or more characteristics, which may be characteristics of a speaker, his speech, his environment, or the speech channel used to communicate with the speaker. The characteristic may be detected either during the dialog or at a later time based on stored data representing the dialog. If the characteristic is detected during the dialog, the dialog can be customized for the speaker at an application level, based on the detected characteristic. The customization may include customization of operations and features such as call routing, error recovery, call flow, content selection, system prompts, or system persona. Data indicative of detected characteristics can be stored and accumulated for many speakers and/or dialogs and analyzed offline to generate a demographic or other type of analysis of the speakers or dialogs with respect to one or more detected characteristics.

Abstract: A voice authentication system having a cognitive recall mechanism for password verification is provided. A user is enrolled for password verification by receiving a first voice input from the user representing the password prompt and a second voice input representing a correct response to the password prompt. The first and second voice inputs may be stored as waveforms, as voiceprints, recognized speech data, or a combination thereof. During verification, the identity of the user is verified by outputting the user-provided password prompt and evaluating a response to password prompt against the correct response. Thus, the user is able to select his own password prompt to facilitate cognitive recall of the password during a subsequent verification phase.

Abstract: A system which uses automatic speech recognition to provide dialogs with human speakers automatically detects one or more characteristics, which may be characteristics of a speaker, his speech, his environment, or the speech channel used to communicate with the speaker. The characteristic may be detected either during the dialog or at a later time based on stored data representing the dialog. If the characteristic is detected during the dialog, the dialog can be customized for the speaker at an application level, based on the detected characteristic. The customization may include customization of operations and features such as call routing, error recovery, call flow, content selection, system prompts, or system persona. Data indicative of detected characteristics can be stored and accumulated for many speakers and/or dialogs and analyzed offline to generate a demographic or other type of analysis of the speakers or dialogs with respect to one or more detected characteristics.

Abstract: Adverse effects of type mismatch between acoustic input devices used during testing and during training in machine-based recognition of the source of acoustic phenomena are minimized. A normalizing model is matched to a source model based, or dependent, upon an acoustic input device whose transfer characteristics color acoustic characteristics of a source as represented in the source model. An application of the present invention is to speaker recognition, i.e., recognition of the identity of a speaker by the speaker's voice.