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{\large \bf How May I Help You?": Automated Customer Service via Natural Spoken Dialog}\\[+6pt]
{\it Alicia Abella, Allen Gorin, Giuseppe Riccardi, Jeremey Wright, Tirso Alonso}\\
AT\&T Shannon Laboratories, 180 Park Ave. Florham Park, New Jersey 07932\\


The next generation of voice-based user interfaces will enable
easy-to-use automation of new and existing communication services. A
critical issue is to move away from highly-structured menus to a more
natural human-machine paradigm.  In this tutorial we will cover the
large vocabulary speech recognition, language modeling, spoken
language understanding, dialog manager and logging functionalities of
our system. We will show how finite state representation and
stochastic modeling provide rich tools to model different language
models: n-grams, word phrases, word classes, phrase grammars. We will
also present our latest results on automatically learned
head-dependency grammars and speech disfluency-based language models.
The Spoken Language Understanding (SLU) is based on salient grammar
fragments, acquired automatically from a corpus of transcribed and
labelled training utterances.  Each grammar fragment represents a
cluster of similarly-meaningful phrases, represented as a finite state
machine.  Matches of these to the recognizer output for a test
utterance are grouped in semantically coherent ways, and the best
interpretation of the utterance is found, taking account of dialog
context.  Based on the output of the SLU the dialog manager needs to
determine whether to ask the customer a question, create a database
query, transfer a call, etc. The dialog manager is flexible enough to
be utilized in a wide variety of applications. The dialog manager is
built from general dialog principles that are captured quantitatively
using a Construct Algebra and a task representation that not only
structures the task knowledge but also influences the behavior of the
dialog manager and utilizes the object-oriented paradigm.  The HMIHY
platform has an extensive array of instrumentation built-in to track
its internal operation. The collected information is logged in files
for later analysis. The analysis tool used on these log files is
object oriented, modular, reusable, and extensible. The collected data
includes such things as prompts selected by the dialog manager to
play, audio fed to the ASR engine, etc.  Each of the aforementioned
components was initially integrated into a prototype in 1997 that
automated over 10,000 customer requests for operator services. This
year a wizard-of-oz version of the system for customer care conducted
more than 25,000 dialogs. Based on this data collection, a fully
autonomous system has been deployed in the AT\&T network to handle
customer care requests.

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