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Learn about the advancements in speech recognizer and parser, overcoming challenges in data training, author recognition, and result presentation. Enhanced backend and NLG capabilities for improved user interaction.
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Progress of Interspeech 2005 Information Line By Team Infoline April 24, 2006
Speech Recognizer and Robust Parser • Speech Recognizer • Challenging Situation • No Training data, • Names are there. • Temp Solution • New Language Model is trained based on generated corpora • Significantly trimmed down the number of authors to recognize (From 200 to 30) • Few author names are easily recognized still. • Robust Parser • Loosen up to deal with irregularities of SR output
DM • Allow disambiguation using author name and session name • Taken care of different scenarios of results • If there is no results, • Say Sorry and restart. • If there is one result • Present the detail of the paper, • Then ask whether to present the abstract of the paper • If there is less than or equal to 5 results • Tell the user the number of papers found • Then ask whether to present the summary of the paper. • (List of titles of the paper) • If there is more than 5 results • Say sorry
Backend and NLG • Backend • (may be for this demo only) • SQL-based • Could do author-search and session-name-search • NLG • Fill in all sorts of prompts • A lot of Implicit Confirmation and Explicit Confirmation are missing • That caused a lot of “” in the system
Other small things We Hacked Out • Confidence of The Recognizer • Audio Server is hacked such that • We are always “confident” about the results. • Annoying restarting issue • Commented the restarting routine in Windows • Speech Rec (We really fixed these) • SVQ could now be used. • Memory reallocation is fixed
Demo • Scenario • A user want to know the papers written by • Alan Black • Julia Hirschberg and • Andrew Rosenberg • What it shows • How bad recognition is taken care now. • What happened when the number of answers returned are multiple or single.