| Patent application number | Description | Published |
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| 20090030683 | SYSTEM AND METHOD FOR TRACKING DIALOGUE STATES USING PARTICLE FILTERS - Disclosed are methods, systems, and computer-readable media for tracking dialog states in a spoken dialog system. The method comprises casting a plurality of dialog states, or particles, as a network describing the probability relationships between each of a plurality of variables, sampling a subset of the plurality of dialog states, or particles, in the network, for each sampled dialog state, or particle, projecting into the future, assigning a weight to each sampled particle, and normalizing the assigned weights to yield a new estimated distribution over each variable's values, wherein the distribution over the variables is used in a spoken dialog system. Also disclosed is a method of tuning performance of the methods, systems, and computer-readable media by adding or removing particles to/from the network. | 01-29-2009 |
| 20090112586 | SYSTEM AND METHOD OF EVALUATING USER SIMULATIONS IN A SPOKEN DIALOG SYSTEM WITH A DIVERSION METRIC - Systems, methods and computer-readable media associated with using a divergence metric to evaluate user simulations in a spoken dialog system. The method employs user simulations of a spoken dialog system and includes aggregating a first set of one or more scores from a real user dialog, aggregating a second set of one or more scores from a simulated user dialog associated with a user model, determining a similarity of distributions associated with each of the first set and the second set, wherein the similarity is determined using a divergence metric that does not require any assumptions regarding a shape of the distributions. It is preferable to use a Cramér-von Mises divergence. | 04-30-2009 |
| 20090112598 | SYSTEM AND METHOD FOR APPLYING PROBABILITY DISTRIBUTION MODELS TO DIALOG SYSTEMS IN THE TROUBLESHOOTING DOMAIN - Disclosed herein are systems, methods, and computer-readable media for troubleshooting based on a probability distribution model. The method for troubleshooting based on a probability distribution model includes establishing a speech-based channel of interaction, establishing at least one non-speech-based channel of interaction, maintaining a probability distribution over time for each of a plurality of component variables describing the state of the product or service and state of the conversation, and troubleshooting a product or service by responding based on the probability distribution. | 04-30-2009 |
| 20100138215 | SYSTEM AND METHOD FOR USING ALTERNATE RECOGNITION HYPOTHESES TO IMPROVE WHOLE-DIALOG UNDERSTANDING ACCURACY - Disclosed herein are systems, computer-implemented methods, and tangible computer-readable media for using alternate recognition hypotheses to improve whole-dialog understanding accuracy. The method includes receiving an utterance as part of a user dialog, generating an N-best list of recognition hypotheses for the user dialog turn, selecting an underlying user intention based on a belief distribution across the generated N-best list and at least one contextually similar N-best list, and responding to the user based on the selected underlying user intention. Selecting an intention can further be based on confidence scores associated with recognition hypotheses in the generated N-best lists, and also on the probability of a user's action given their underlying intention. A belief or cumulative confidence score can be assigned to each inferred user intention. | 06-03-2010 |
| 20110010164 | SYSTEM AND METHOD FOR GENERATING MANUALLY DESIGNED AND AUTOMATICALLY OPTIMIZED SPOKEN DIALOG SYSTEMS - Disclosed herein are systems, computer-implemented methods, and tangible computer-readable storage media for generating a natural language spoken dialog system. The method includes nominating a set of allowed dialog actions and a set of contextual features at each turn in a dialog, and selecting an optimal action from the set of nominated allowed dialog actions using a machine learning algorithm. The method includes generating a response based on the selected optimal action at each turn in the dialog. The set of manually nominated allowed dialog actions can incorporate a set of business rules. Prompt wordings in the generated natural language spoken dialog system can be tailored to a current context while following the set of business rules. A compression label can represent at least one of the manually nominated allowed dialog actions. | 01-13-2011 |
| 20110054893 | SYSTEM AND METHOD FOR GENERATING USER MODELS FROM TRANSCRIBED DIALOGS - Disclosed herein are systems, computer-implemented methods, and computer-readable storage media for generating personalized user models. The method includes receiving automatic speech recognition (ASR) output of speech interactions with a user, receiving an ASR transcription error model characterizing how ASR transcription errors are made, generating guesses of a true transcription and a user model via an expectation maximization (EM) algorithm based on the error model and the respective ASR output where the guesses will converge to a personalized user model which maximizes the likelihood of the ASR output. The ASR output can be unlabeled. The method can include casting speech interactions as a dynamic Bayesian network with four variables: (s), (u), (r), (m), and encoding relationships between (s), (u), (r), (m) as conditional probability tables. At each dialog turn (r) and (m) are known and (s) and (u) are hidden. | 03-03-2011 |
| 20110099012 | SYSTEM AND METHOD FOR ESTIMATING THE RELIABILITY OF ALTERNATE SPEECH RECOGNITION HYPOTHESES IN REAL TIME - Disclosed herein are systems, methods, and computer-readable storage media for estimating reliability of alternate speech recognition hypotheses. A system configured to practice the method receives an N-best list of speech recognition hypotheses and features describing the N-best list, determines a first probability of correctness for each hypothesis in the N-best list based on the received features, determines a second probability that the N-best list does not contain a correct hypothesis, and uses the first probability and the second probability in a spoken dialog. The features can describe properties of at least one of a lattice, a word confusion network, and a garbage model. In one aspect, the N-best lists are not reordered according to reranking scores. The determination of the first probability of correctness can include a first stage of training a probabilistic model and a second stage of distributing mass over items in a tail of the N-best list. | 04-28-2011 |
| 20110131048 | SYSTEM AND METHOD FOR AUTOMATICALLY GENERATING A DIALOG MANAGER - Disclosed herein are systems, methods, and computer-readable storage media for automatically generating a dialog manager for use in a spoken dialog system. A system practicing the method receives a set of user interactions having features, identifies an initial policy, evaluates all of the features in a linear evaluation step of the algorithm to identify a set of most important features, performs a cubic policy improvement step on the identified set of most important features, repeats the previous two steps one or more times, and generates a dialog manager for use in a spoken dialog system based on the resulting policy and/or set of most important features. Evaluating all of the features can include estimating a weight for each feature which indicates how much each feature contributes to at least one of the identified policies. The system can ignore features not in the set of most important features. | 06-02-2011 |
| 20110137654 | SYSTEM AND METHOD FOR EFFICIENT TRACKING OF MULTIPLE DIALOG STATES WITH INCREMENTAL RECOMBINATION - Disclosed herein are systems, methods, and computer-readable storage media for tracking multiple dialog states. A system practicing the method receives an N-best list of speech recognition candidates, a list of current partitions, and a belief for each of the current partitions. A partition is a group of dialog states. In an outer loop, the system iterates over the N-best list of speech recognition candidates. In an inner loop, the system performs a split, update, and recombination process to generate a fixed number of partitions after each speech recognition candidate in the N-best list. The system recognizes speech based on the N-best list and the fixed number of partitions. The split process can perform all possible splits on all partitions. The update process can compute an estimated new belief. The estimated new belief can be a product of ASR reliability, user likelihood to produce this action, and an original belief. | 06-09-2011 |
