%0 Conference Paper %1 TorbatiPicone2013 %A Torbati, A. H. Harati Nejad %A Picone, J. %B Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2013 IEEE International Multi-Disciplinary Conference on %D 2013 %K 2006 STD acoustic analysis;Keyword analysis;NIST assessment;spelling;spoken channel;acoustic confusability;search detection;system detection;voice evaluation;acoustic file keyword language modeling;audio processing;information recognition;information recognition;text recording;spoken retrieval;speaker retrieval;spoken search search;NIST;Speech;Speech search;correlation;keyword;search searching;Acoustics;Conferences;Error signal strength term usability;text-based %P 114-117 %R 10.1109/CogSIMA.2013.6523832 %T Assessing search term strength in spoken term detection %X Spoken term detection (STD) is an extension of text-based searching that allows users to type keywords and search audio files containing recordings of spoken language. Performance is dependent on many external factors such as the acoustic channel, the language and the confusability of the search term. Unlike text-based searches, the quality of the search term plays a significant role in the overall perception of the usability of the system. In this paper, we present a system that predicts the strength of a search term from its spelling that is based on an analysis of spoken term detection output from several spoken term detection systems that participated in the NIST 2006 STD evaluation. We show that approximately 57% of the correlation can be explained from the search term, but that a significant amount of the confusability is due to other acoustic modeling issues.

@inproceedings{TorbatiPicone2013, abstract = {Spoken term detection (STD) is an extension of text-based searching that allows users to type keywords and search audio files containing recordings of spoken language. Performance is dependent on many external factors such as the acoustic channel, the language and the confusability of the search term. Unlike text-based searches, the quality of the search term plays a significant role in the overall perception of the usability of the system. In this paper, we present a system that predicts the strength of a search term from its spelling that is based on an analysis of spoken term detection output from several spoken term detection systems that participated in the NIST 2006 STD evaluation. We show that approximately 57% of the correlation can be explained from the search term, but that a significant amount of the confusability is due to other acoustic modeling issues.}, added-at = {2016-05-13T17:49:14.000+0200}, author = {Torbati, A. H. Harati Nejad and Picone, J.}, biburl = {https://www.bibsonomy.org/bibtex/26c23cce65b807294854a46a4a02df75a/templehpc}, booktitle = {Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2013 IEEE International Multi-Disciplinary Conference on}, doi = {10.1109/CogSIMA.2013.6523832}, interhash = {c70d4337e9edb1cbbf8cd61bd57ef814}, intrahash = {6c23cce65b807294854a46a4a02df75a}, keywords = {2006 STD acoustic analysis;Keyword analysis;NIST assessment;spelling;spoken channel;acoustic confusability;search detection;system detection;voice evaluation;acoustic file keyword language modeling;audio processing;information recognition;information recognition;text recording;spoken retrieval;speaker retrieval;spoken search search;NIST;Speech;Speech search;correlation;keyword;search searching;Acoustics;Conferences;Error signal strength term usability;text-based}, month = Feb, pages = {114-117}, timestamp = {2016-05-13T17:49:46.000+0200}, title = {Assessing search term strength in spoken term detection}, year = 2013 }