Technical Program

Paper Detail

Paper:SP-L11.1
Session:Language Modeling and Search
Time:Friday, May 21, 15:30 - 15:50
Presentation: Lecture
Topic: Speech Processing: Language Modeling
Title: META-DATA CONDITIONAL LANGUAGE MODELING
Authors: Michiel Bacchiani; AT&T Labs - Research 
 Brian Roark; AT&T Labs - Research 
Abstract: Automatic Speech Recognition (ASR) often occurs in circumstances in which knowledge external to the speech signal, or meta-data, is given. For example, a company receiving a call from a customer might have access to a database record of that customer. Conditioning the ASR models directly on this information to improve the transcription accuracy is hampered because, generally, the meta-data takes on many values and a training corpus will have little data for each meta-data condition. This paper presents an algorithm to construct language models conditioned on such meta-data. It uses tree-based clustering of the the training data to automatically derive meta-data projections, useful as language model conditioning contexts. The algorithm was tested on a multiple domain voicemail transcription task. We compare the performance of an adapted system aware of the domain shift to a system that only has meta-data to infer that fact. The meta-data used were the callerID strings associated with the voicemail messages. The meta-data adapted system matched the performance of the system adapted using the domain knowledge explicitly.
 
           Back


Home -||- Organizing Committee -||- Technical Committee -||- Technical Program -||- Plenaries
Paper Submission -||- Special Sessions -||- ITT -||- Paper Review -||- Exhibits -||- Tutorials
Information -||- Registration -||- Travel Insurance -||- Housing -||- Workshops

©2015 Conference Management Services, Inc. -||- email: webmaster@icassp2004.org -||- Last updated Wednesday, April 07, 2004