Jeremy H. Wright

Grammar fragment acquisition using syntactic and semantic clustering

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.

Optimizing SVMs for complex call classification

Large margin classifiers such as support vector machines (SVM) or Adaboost are obvious choices for natural language document or call routing. However, how to combine several binary classifiers to optimize the whole routing process and how this process scales when it involves many different decisions (or classes) is a complex problem that has only received partial answers. We propose a global optimization process based on an optimal channel communication model that allows a combination of possibly heterogeneous binary classifiers. As in Markov modeling, computational feasibility is achieved through simplifications and independence assumptions that are easy to interpret. Using this approach, we have managed to decrease the call-type classification error rate for AT&Ts How May I Help You (HMIHY/sup (sm)/) natural dialog system by 50 %.

Automatically training a problematic dialogue predictor for a spoken dialogue system

Spoken dialogue systems promise efficient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the How May I Help YouSM spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the systems decision of whether to transfer the call to a human customer care agent, or be used as a cue to the systems dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automatically-obtainable features from the first two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.

Detecting and extracting named entities from spontaneous speech in a mixed-initiative spoken dialogue context: How May I Help You?sm,tm

The understanding module of a spoken dialogue system must extract, from the speech recognizer output, the kind of request expressed by the caller (the call type) and its parameters (numerical expressions, time expressions or propernames). Such expressions are called Named Entities and their definitions can be either generic or linked to the dialogue application domain. Detecting and extracting such Named Entities within a mixed-initiative dialogue context like How May I Help You? sm;tm (HMIHY) is the subject of this study. After reviewing standard methods based on hand-written grammars and statistical tagging, we propose a new approach, combining the advantages of both in a 2-step process. We also propose a novel architecture which exploits understanding to improve recognition accuracy: the output of the Automatic Speech Recognition module is now a word lattice and the understanding module is responsible for transcribing the word strings which are useful to the Dialogue Manager. All the methods proposed are trained and evaluated on a corpus comprising utterances from live customer traffic. � 2003 Elsevier B.V. All rights reserved.

Method and system for predicting understanding errors in automated dialog systems

This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a users input communications. The method includes determining whether a probability of understanding the users 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.

Integration of utterance verification with statistical language modeling and spoken language understanding

Methods for utterance verification (UV) and their integration into statistical language modeling and understanding formalisms for a large vocabulary spoken understanding system are presented. The paper consists of three parts. First, a set of acoustic likelihood ratio (LR) based UV techniques are described and applied to the problem of rejecting portions of a hypothesized word string that may have been incorrectly decoded by a large vocabulary continuous speech recognizer. Second, a procedure for integrating the acoustic level confidence measures with the statistical language model is described. Finally, the effect of integrating acoustic level confidence into the spoken language understanding unit (SLU) in a call-type classification task is discussed. These techniques were evaluated on utterances collected from a highly unconstrained call routing task performed over the telephone network. They have been evaluated in terms of their ability to classify utterances into a set of 15 call-types that are accepted by the application.

CoCITe—Coordinating Changes in Text

Text streams are ubiquitous and contain a wealth of information, but are typically orders of magnitude too large in scale for comprehensive human inspection. There is a need for tools that can detect and group changes occurring within text streams and substreams, in order to find, structure, and summarize these changes for presentation to human analysts. This paper describes a procedure for efficiently finding step changes, trends, bursts, and cyclic changes affecting frequencies of words, or more general lexical items, within streams of documents which may be optionally labeled with metadata. The common phenomenon of over-dispersion is accommodated using mixture distributions. A streaming implementation is described which can process data from a continuous feed. Anomalies can be detected, grouped, and rendered visually for human comprehension.