Abstract: This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a user's input communications. The method illustratively determines whether a probability of understanding the user's input communication exceeds a first threshold. If the first threshold is exceeded, further dialog is conducted with the user. Otherwise, the user may be directed to a human for assistance. The method also illustratively determines whether the probability also exceeds a second threshold, the second threshold being higher than the first. If so, then further dialog is conducted with the user using the current dialog strategy. However, if the probability falls between a first threshold and a second threshold, the dialog strategy may be adapted in order to improve the chances of conducting a successful dialog with the user.

Abstract: A spoken dialog system and method having a dialog management module are disclosed. The dialog management module includes a plurality of dialog motivators for handling various operations during a spoken dialog. The dialog motivators comprise an error handling, disambiguation, assumption, confirmation, missing information, and continuation. The spoken dialog system uses the assumption dialog motivator in either a-priori or a-posteriori modes. A-priori assumption is based on predefined requirements for the call flow and a-posteriori assumption can work with the confirmation dialog motivator to assume the content of received user input and confirm received user input.

Abstract: The invention concerns a method and system for detecting morphemes in a user's communication. The method may include recognizing a lattice of phone strings from the user's input communication, the lattice representing a distribution over the phone strings, and detecting morphemes in the user's input communication using the lattice. The morphemes may be acoustic and/or non-acoustic. The morphemes may represent any unit or sub-unit of communication including phones, diphones, phone-phrases, syllables, grammars, words, gestures, tablet strokes, body movements, mouse clicks, etc. The training speech may be verbal, non-verbal, a combination of verbal and non-verbal, or multimodal.

Abstract: The invention concerns a method and system for detecting morphemes in a user's communication. The method may include recognizing a lattice of phone strings from the user's input communication, the lattice representing a distribution over the phone strings, and detecting morphemes in the user's input communication using the lattice. The morphemes may be acoustic and/or non-acoustic. The morphemes may represent any unit or sub-unit of communication including phones, diphones, phone-phrases, syllables, grammars, words, gestures, tablet strokes, body movements, mouse clicks, etc. The training speech may be verbal, non-verbal, a combination of verbal and non-verbal, or multimodal.

Abstract: A spoken dialog system and method having a dialog management module are disclosed. The dialog management module includes a plurality of dialog motivators for handling various operations during a spoken dialog. The dialog motivators comprise an error handling, disambiguation, assumption, confirmation, missing information, and continuation. The spoken dialog system uses the assumption dialog motivator in either a-priori or a-posteriori modes. A-priori assumption is based on predefined requirements for the call flow and a-posteriori assumption can work with the confirmation dialog motivator to assume the content of received user input and confirm received user input.

Abstract: Systems and methods for unsupervised segmentation of multi-speaker speech or audio data by speaker. A front-end analysis is applied to input speech data to obtain feature vectors. The speech data is initially segmented and then clustered into groups of segments that correspond to different speakers. The clusters are iteratively modeled and resegmented to obtain stable speaker segmentations. The overlap between segmentation sets is checked to ensure successful speaker segmentation. Overlapping segments are combined and remodeled and resegmented. Optionally, the speech data is processed to produce a segmentation lattice to maximize the overall segmentation likelihood.

Abstract: The invention concerns a method and system for detecting morphemes in a user's communication. The method may include recognizing a lattice of phone strings from the user's input communication, the lattice representing a distribution over the phone strings, and detecting morphemes in the user's input communication using the lattice. The morphemes may be acoustic and/or non-acoustic. The morphemes may represent any unit or sub-unit of communication including phones, diphones, phone-phrases, syllables, grammars, words, gestures, tablet strokes, body movements, mouse clicks, etc. The training speech may be verbal, non-verbal, a combination of verbal and non-verbal, or multimodal.

Abstract: Systems and methods for monitoring labelers of speech data. To test or train labelers, a labeler is presented with utterances that have already been identified as belonging to a particular class or call type. The labeler is asked to assign a call type to the utterances. The performance of the labeler is measured by comparing the call types assigned by the labeler with the existing call types of the utterances. The performance of a labeler can also be monitored as the labeler labels speech data by occasionally having the labeler label an utterance that is already labeled and by storing the results.

Abstract: Systems and methods for generating an annotation guide. Speech data is organized and presented to a user. After the user selects some of the utterances in the speech data, the selected utterances are included in a class and/or call type. Additional utterances that belong to the class and/or call type can be found in the speech data using relevance feedback, data mining, data clustering, support vector machines, and the like. After a call type is complete, it is committed to the annotation guide. After all call types are completed, the annotation guide is generated.

Abstract: A system for recognizing connected digits in natural spoken dialogue includes a speech recognition processor that receives unconstrained fluent input speech and produces a string of words that can include a numeric language, and a numeric understanding processor that converts the string of words into a sequence of digits based on a set of rules. An acoustic model database utilized by the speech recognition processor includes a first set of hidden Markov models that characterize the acoustic features of numeric words and phrases, a second set of hidden Markov models that characterize the acoustic features of the remaining vocabulary words, and a filler model that characterizes the acoustic features of out-of-vocabulary utterances. An utterance verification processor verifies the accuracy of the string of words. A validation database stores a grammar, and a string validation processor outputs validity information based on a comparison of the sequence of digits with the grammar.

Abstract: A spoken dialog system and method having a dialog management module are disclosed. The dialog management module includes a plurality of dialog motivators for handling various operations during a spoken dialog. The dialog motivators comprise an error-handling, disambiguation, assumption, confirmation, missing information, and continuation. The spoken dialog system uses the assumption dialog motivator in either a-priori or a-posteriori modes. A-priori assumption is based on predefined requirements for the call flow and a-posteriori assumption can work with the confirmation dialog motivator to assume the content of received user input and confirm received user input.

Abstract: The invention concerns a system and method of predicting problematic dialogs in an automated dialog system based on the user's input communications. The method may include determining whether a probability of conducting a successful dialog with the user exceeds a first threshold. The successful dialog may be defined as a dialog exchange between an automated dialog system and the user that results in at least one of processing of the user's input communication and routing the user's input communication. The method may further operate such that if the first threshold is exceeded, further dialog is conducted with the user. Otherwise, the user may be directed to a human for assistance.

Abstract: State-of-the-art speech recognition systems are trained using transcribed utterances, preparation of which is labor-intensive and time-consuming. The present invention is an iterative method for reducing the transcription effort for training in automatic speech recognition (ASR). Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples and then selecting the most informative ones with respect to a given cost function for a human to label. The method comprises automatically estimating a confidence score for each word of the utterance and exploiting the lattice output of a speech recognizer, which was trained on a small set of transcribed data. An utterance confidence score is computed based on these word confidence scores; then the utterances are selectively sampled to be transcribed using the utterance confidence scores.

Abstract: The invention concerns a method of generating morphemes for speech recognition and understanding. The method may include receiving training speech, selecting candidate sub-morphemes from the training speech, selecting salient sub-morphemes from the candidate sub-morphemes based on salience measurements, and clustering the salient sub-morphemes based on semantic and syntactic similarities into morphemes. The morphemes may be acoustic and/or non-acoustic. The sub-morphemes may represent any sub-unit of communication including phones, phone-phrases, grammars, diphones, words, gestures, tablet strokes, body movements, mouse clicks, etc. The training speech may be verbal, non-verbal, a combination of verbal and non-verbal, or multimodal.

Abstract: A spoken dialog system having a dialog management module is disclosed. The dialog management module includes a plurality of dialog motivators for handling various operations during a spoken dialog. The dialog motivators comprise error-handling, disambiguation, assumption, confirmation, missing information, and continuation. The spoken dialog system uses the assumption dialog motivator in either a-priori or a-posteriori modes. A-priori assumption is based on predefined requirements for the call flow and a-posteriori assumption can work with the confirmation dialog motivator to assume the content of received user input and confirm received user input.

Abstract: This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a user's input communications in a task classification system. The method may include determining whether the user's input communication can be understood in order to make a task classification decision. If the user's input communication cannot be understood and a task classification decision cannot be made, a probability of understanding the user's input communication may be determined. If the probability exceeds a first threshold, further dialog may be conducted with the user. Otherwise, the user may be directed to a human for assistance. In another possible embodiment, the method operates as above except that if the probability exceeds a second threshold, the second threshold being higher than the first, then further dialog may be conducted with the user using the current dialog strategy.

Abstract: The invention concerns a method of task classification using morphemes which operates on the task objective of a user. The morphemes may be generated by clustering selected ones of the salient sub-morphemes selected from training speech which are semantically and syntactically similar. The method may include detecting morphemes present in the user's input communication, and making task-type classification decisions based on the detected morphemes in the user's input communication. The morphemes may be verbal and/or non-verbal.

Abstract: This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a user's input communications. The method includes determining whether a probability of understanding the user's input communication exceeds a first thresholds, where if the first threshold is exceeded, further dialog is conducted with the user. Otherwise, the user may be directed to a human for assistance. In another possible embodiment, the method operates as above except that if the probability also exceeds a second threshold, the second threshold being higher than the first, then further dialog is conducted with the user using the current dialog strategy. However, if the probability falls between a first threshold and a second threshold, the dialog strategy may be adapted in order to improve the chances of conducting a successful dialog with the user.

Abstract: The invention concerns a system and method of predicting problematic dialogs in a task classification system based on the user's input communications. The method may include determining whether a task classification decision can be made based on a first automated dialog exchange with the user. As such, if the task classification decision cannot be made, the method may determine whether the probability of conducting a successful automated dialog with the user based on whether the first dialog exchange exceeds a first threshold. The successful dialog may be defined as a dialog exchange between an automated dialog system and the user that results in at least one of processing of the user's input communication and routing the user's input communication. The method may further operate such that if the first threshold is exceeded, further dialog is conducted with the user. Otherwise, the user may be directed to a human for assistance.

Abstract: This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a user's input communications in a task classification system. The method may include determining whether the user's input communication can be understood in order to make a task classification decision. If the user's input communication cannot be understood and a task classification decision cannot be made, a probability of understanding the user's input communication may be determined. If the probability exceeds a first threshold, further dialog may be conducted with the user. Otherwise, the user may be directed to a human for assistance. In another possible embodiment, the method operates as above except that if the probability exceeds a second threshold, the second threshold being higher than the first, then further dialog may be conducted with the user using the current dialog strategy.