At+t Corp.

At&t Intellectual Property I, L.p.

Lucent Technologies Inc.

Nippon Telegraph and Telephone Corporation

Nuance Communications, Inc.

Interactions Corporation

Allen Louis Gorin

Jeremy Huntley Wright

Giuseppe Riccardi

Marilyn Ann Walker

Irene Langkilde Geary

Mazin G Rahim

Zhu Liu

Dijana Petrovska-Delacretaz

David Crawford Gibbon

Behzad Shahraray

Lee Begeja

Bernard S Renger

Patrick Guy Haffner

Steven Hart Lewis

Harris Drucker

Dilek Zeynep Hakkani-Tur

Alicia Abella

Bruce Melvin Buntschuh

Richard Vandervoort Cox

Jay Gordon Wilpon

John Grothendieck

Kazuhiro Arai

Diane Judith Litman

Jeremy Huntley Greet Wright

Barry Allen Parker

James B Scherer

Gokhan Tur

Sarangarajan Parthasarathy

Aaron Edward Rosenberg

Egbert Ammicht

Guiseppe Riccardi

Frederic Bechet

An apparatus, a method, and a machine-readable medium are provided for characterizing differences between two language models. A group of utterances from each of a group of time domains are examined. One of a significant word change or a significant word class change within the plurality of utterances is determined. A first cluster of utterances including a word or a word class corresponding to the one of the significant word change or the significant word class change is generated from the utterances. A second cluster of utterances not including the word or the word class corresponding to the one of the significant word change or the significant word class change is generated from the utterances.

Apparatus and method for analysis of language model changes

A method and apparatus are provided for automatically acquiring grammar fragments for recognizing and understanding fluently spoken language. Grammar fragments representing a set of syntactically and semantically similar phrases may be generated using three probability distributions: of succeeding words, of preceding words, and of associated call-types. The similarity between phrases may be measured by applying Kullback-Leibler distance to these tree probability distributions. Phrases being close in all three distances may be clustered into a grammar fragment.

Grammar fragment acquisition using syntactic and semantic clustering

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.

Method of active learning for automatic speech recognition

A system and a method are provided. A speech recognition processor receives unconstrained input speech and outputs a string of words. The speech recognition processor is based on a numeric language that represents a subset of a vocabulary. The subset includes a set of words identified as being for interpreting and understanding number strings. A numeric understanding processor contains classes of rules for converting the string of words into a sequence of digits. The speech recognition processor utilizes an acoustic model database. A validation database stores a set of valid sequences of digits. A string validation processor outputs validity information based on a comparison of a sequence of digits output by the numeric understanding processor with valid sequences of digits in the validation database.

Recognizing the numeric language in natural spoken dialogue

A method and apparatus are provided for automatically acquiring grammar fragments for recognizing and understanding fluently spoken language. Grammar fragments representing a set of syntactically and semantically similar phrases may be generated using three probability distributions: of succeeding words, of preceding words, and of associated call-types. The similarity between phrases may be measured by applying Kullback-Leibler distance to these three probability distributions. Phrases being close in all three distances may be clustered into a grammar fragment.

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.

Method for dialog management

In an embodiment, a lattice of phone strings in an input communication of a user may be recognized, wherein the lattice may represent a distribution over the phone strings. Morphemes in the input communication of the user may be detected using the recognized lattice. Task-type classification decisions may be made based on the detected morphemes in the input communication of the user.

Method and system for automatically detecting morphemes in a task classification system using lattices

Systems and methods herein use an annotation guide to label utterances and speech data with a call type. A system practicing the method embodiment monitors labelers of speech data by presenting via a processor a test utterance to a labeler, receiving input from the labeler that selects a particular call type from a list of call types and determining via the processor if the labeler labeled the test utterance correctly. Based on the determining step, the system revises the annotation guide, retrains the labeler, and/or alters the test utterance.

Systems and methods for monitoring speech data labelers

The invention concerns an automated task classification system that operates on a task objective of a user. The system may include a meaningful phrase generator that generates a plurality of meaningful phrases from a set of verbal and non-verbal speech. Each of the meaningful phrases may be generated based on one of a predetermined set of the task objectives. A recognizer may recognize at least one of the generated meaningful phrases in an input communication of the user and a task classifier may make a classification decision in response to the recognized meaningful phrases relating to one of the set of predetermined task objectives.

Automated task classification system

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. If the user's input communication cannot be understood and a task classification decision cannot be made, then further dialog may be conducted with the user if a probability of understanding the user's input communication exceeds a first threshold. 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, then further dialog may be 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.

Method and system for predicting understanding errors in a task classification system

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Berkeley Heights, NJ, United States of America

Allen Louis Gorin