Abstract: A method of receiving input from a user includes sensing a first trajectory of a center of mass of a hand of the user during a gesture made by the hand. A second trajectory of a finger tip of the hand of the user during the gesture made by the hand is also sensed. An alphanumeric character represented by the gesture made by the hand is determined dependent upon both the first trajectory and the second trajectory.

Abstract: Embodiments of a dialog system that utilizes contextual information to perform recognition of proper names are described. Unlike present name recognition methods on large name lists that generally focus strictly on the static aspect of the names, embodiments of the present system take into account of the temporal, recency and context effect when names are used, and formulates new questions to further constrain the search space or grammar for recognition of the past and current utterances.

Abstract: Systems and methods are described that automatically control modules of dialog systems. The systems and methods include a dialog module that receives and processes utterances from a speaker and outputs data used to generate synthetic speech outputs as responses to the utterances. A controller is coupled to the dialog module, and the controller detects an abnormal output of the dialog module when the dialog module is processing in an automatic mode. The controller comprises a mode control for an agent to control the dialog module by correcting the abnormal output and transferring a corrected output to a downstream dialog module that follows, in a processing path, the dialog module. The corrected output is used in further processing the utterances.

Abstract: Embodiments of a method and system for detecting repeated patterns in dialog systems are described. The system includes a dynamic time warping (DTW) based pattern comparison algorithm that is used to find the best matching parts between a correction utterance and an original utterance. Reference patterns are generated from the correction utterance by an unsupervised segmentation scheme. No significant information about the position of the repeated parts in the correction utterance is assumed, as each reference pattern is compared with the original utterance from the beginning of the utterance to the end. A pattern comparison process with DTW is executed without knowledge of fixed end-points. A recursive DTW computation is executed to find the best matching parts that are considered as the repeated parts as well as the end-points of the utterance.

Abstract: Embodiments of a dialog system that utilizes contextual information to perform recognition of proper names are described. Unlike present name recognition methods on large name lists that generally focus strictly on the static aspect of the names, embodiments of the present system take into account of the temporal, recency and context effect when names are used, and formulates new questions to further constrain the search space or grammar for recognition of the past and current utterances.

Abstract: An optimal selection or decision strategy is described through an example that includes use in dialog systems. The selection strategy or method includes receiving multiple predictions and multiple probabilities. The received predictions predict the content of a received input and each of the probabilities corresponds to one of the predictions. In an example dialog system, the received input includes an utterance. The selection method includes dynamically selecting a set of predictions from the received predictions by generating ranked predictions. The ranked predictions are generated by ordering the plurality of predictions according to descending probability.

Abstract: An optimal selection or decision strategy is described through an example that includes use in dialog systems. The selection strategy or method includes receiving multiple predictions and multiple probabilities. The received predictions predict the content of a received input and each of the probabilities corresponds to one of the predictions. In an example dialog system, the received input includes an utterance. The selection method includes dynamically selecting a set of predictions from the received predictions by generating ranked predictions. The ranked predictions are generated by ordering the plurality of predictions according to descending probability.

Abstract: Embodiments of a progressive feature selection method that selects features in multiple rounds are described. In one embodiment, the progressive feature selection method splits the feature space into tractable sub-spaces such that a feature selection algorithm can be performed on each sub-space. In a merge-split operation, the subset of features that the feature selection algorithm selects from the different sub-spaces are merged into subsequent sets of features. Instead of re-generating the mapping table for each subsequent set from scratch, a new mapping table from the previous round's tables is created by collecting those entries that correspond to the selected features. The feature selection method is then performed again on each of the subsequent feature sets and new features are selected from each of these feature sets. This feature selection-merge-split process is repeated on successively smaller numbers of feature sets until a single final set of features is selected.

Abstract: Embodiments of a feature generation system and process for use in machine learning applications utilizing statistical modeling systems are described. In one embodiment, the feature generation process generates large feature spaces by combining features using logical, arithmetic and/or functional operations. A first set of features in an initial feature space are defined. Some or all of the first set of features are processed using one or more arithmetic, logic, user-defined combinatorial processes, or combinations thereof, to produce additional features. The additional features and at least some of the first set of features are combined to produce an expanded feature space. The expanded feature space is processed through a feature selection and optimization process to produce a model in a statistical modeling system.

Abstract: Systems and methods are described that automatically generate interactive systems configured for collecting dialog data of human-machine interactions in dialog systems. The systems and methods comprise receiving a task flow that describes operations of a dialog system. A formal description of the task flow is generated, and an interactive system comprising a graphical user interface (GUI) is automatically generated from the formal description. The GUI consists of templates for control of the dialog system and real-time collection and annotating of dialog data during a live dialog between only the dialog system and callers to the dialog system. The dialog data consists of data of the live dialog.

Abstract: A method of displaying a navigation map includes determining a route of a vehicle. Three-dimensional data is received. The data is associated with buildings that are disposed within a geographic area. The geographic area includes the route of the vehicle. At least one of the buildings in the data that is adjacent to at least one street of the route is rendered in a first opaque visual style. All of the buildings in the data that are nonadjacent to at least one street of the route are rendered in at least one second visual style different from the first opaque visual style.

Abstract: An advanced model that includes new processes is provided for use as a component of an effective disfluency identifier. The disfluency identifier tags edited words in transcribed speech. A speech recognition unit in combination with a part-of-speech tagger, a disfluency identifier, and a parser form a natural language system that helps machines properly interpret spoken utterances.

Abstract: Embodiments of a name recognition process for use in dialog systems are described. In one embodiment, the name recognition process assigns weighting values to names used in a dialog based on the usage of these names. This process takes advantage of the general tendency of people to speak names, either full or partial, only after they have heard or read these names. Name input is taken in several different forms, including a static background database that contains all possible names, a background database that contains commonly used names (such as common trademarks or references), a database that contains names from a user model, and a dynamic database that constantly takes the names just mentioned. The names are then appended with proper weighting values. A high weight is given to names that have been mentioned recently, a lower weight is given to common names, and a lowest weight is given to names for the ones that have never been used or mentioned.

Abstract: Representation-neutral dialogue systems and methods (“RNDS”) are described that include multi-application, multi-device spoken-language dialogue systems based on the information-state update approach. The RNDS includes representation-neutral core components of a dialogue system that provide scripted domain-specific extensions to routines such as dialogue move modeling and reference resolution, easy substitution of specific semantic representations and associated routines, and clean interfaces to external components for language-understanding (i.e., speech-recognition and parsing) and language-generation, and to domain-specific knowledge sources. The RNDS also resolves multi-device dialogue by evaluating and selecting among candidate dialogue moves based on features at multiple levels. Multiple sources of information are combined, multiple speech recognition and parsing hypotheses tested, and multiple device and moves considered to choose the highest scoring hypothesis overall.

Abstract: A method of receiving input from a user includes providing a surface within reach of a hand of the user. A plurality of locations on the surface that are touched by the user are sensed. An alphanumeric character having a shape most similar to the plurality of touched locations on the surface is determined. The user is audibly or visually informed of the alphanumeric character and/or a word in which the alphanumeric character is included. Feedback is received from the user regarding whether the alphanumeric character and/or word is an alphanumeric character and/or word that the user intended to be determined in the determining step.

Abstract: A method for speech recognition includes providing a source of geographical information within a vehicle. The geographical information pertains to a current location of the vehicle, a planned travel route of the vehicle, a map displayed within the vehicle, and/or a gesture marked by a user on a map. Words spoken within the vehicle are recognized by use of a speech recognition module. The recognizing is dependent upon the geographical information.

Abstract: A method and system to parameterize a spoken language dialog system, includes providing a storage area to store at least one parameter value, integrating a subcomponent with at least one module of the spoken language dialog system, and configuring the subcomponent to access the at least one parameter value and to adjust an operation or output of the module based on the at least one parameter value.

Abstract: A method of proper name recognition includes classifying each word of a word string with a tag indicating a proper name entity category or a non-named entity category, and correcting the tag of a boundary word of the word string.

Abstract: A method and system of providing an interactive manual, including a speech engine to receive and process speech from a user, convert the speech into a word sequence, and identify meaning structures from the word sequence, a structured manual including information related to an operation of a device, a visual model to relate visual representation of the information, a dialog management arrangement to interpret the meaning structures in a context and to extract pertinent information and the visual representation from the structured manual and the visual model, and an output arrangement to output the information and visual representation.

Abstract: Embodiments of a dialog system that utilizes a multi-modal input interface for recognizing user input in human-machine interaction (HMI) systems are described. Embodiments include a component that receives user input from a plurality of different user input mechanisms (multi-modal input) and performs certain synchronization and disambiguation processes. The multi-modal input components synchronizes and integrates the information obtained from different modalities, disambiguates the input, and recovers from any errors that might be produced with respect to any of the user inputs. Such a system effectively addresses any ambiguity associated with the user input and corrects for errors in the human-machine interaction.