AUTO-TAGGER THAT LEARNS
Examples perform context categorization by categorizing a transcription of a speech file based on the context of the subject matter of the transcription. A computer processor is configuration to provide a system that generates a normalized transcription of the speech file transcription and compares...
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creator | BROWN DEBORAH WASHINGTON |
description | Examples perform context categorization by categorizing a transcription of a speech file based on the context of the subject matter of the transcription. A computer processor is configuration to provide a system that generates a normalized transcription of the speech file transcription and compares elements of the normalized transcription to elements of a context categorization model or a corpus of categorized transcriptions to determine whether the normalized transcription contains keywords of transcriptions that have previously been categorized. If the comparison yields a result indicating a specific match with a context category, the normalized transcription is assigned to the matching context category. As the number of successfully categorized transcriptions stored in the corpus increases, the more frequently the system and method examples perform successful comparisons. As a result, the context categorization accuracy increases and the system appears to learn. |
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A computer processor is configuration to provide a system that generates a normalized transcription of the speech file transcription and compares elements of the normalized transcription to elements of a context categorization model or a corpus of categorized transcriptions to determine whether the normalized transcription contains keywords of transcriptions that have previously been categorized. If the comparison yields a result indicating a specific match with a context category, the normalized transcription is assigned to the matching context category. As the number of successfully categorized transcriptions stored in the corpus increases, the more frequently the system and method examples perform successful comparisons. As a result, the context categorization accuracy increases and the system appears to learn.</description><language>eng</language><subject>ACOUSTICS ; CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; MUSICAL INSTRUMENTS ; PHYSICS ; SPEECH ANALYSIS OR SYNTHESIS ; SPEECH OR AUDIO CODING OR DECODING ; SPEECH OR VOICE PROCESSING ; SPEECH RECOGNITION</subject><creationdate>2015</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20150604&DB=EPODOC&CC=US&NR=2015154956A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25562,76317</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20150604&DB=EPODOC&CC=US&NR=2015154956A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>BROWN DEBORAH WASHINGTON</creatorcontrib><title>AUTO-TAGGER THAT LEARNS</title><description>Examples perform context categorization by categorizing a transcription of a speech file based on the context of the subject matter of the transcription. A computer processor is configuration to provide a system that generates a normalized transcription of the speech file transcription and compares elements of the normalized transcription to elements of a context categorization model or a corpus of categorized transcriptions to determine whether the normalized transcription contains keywords of transcriptions that have previously been categorized. If the comparison yields a result indicating a specific match with a context category, the normalized transcription is assigned to the matching context category. As the number of successfully categorized transcriptions stored in the corpus increases, the more frequently the system and method examples perform successful comparisons. As a result, the context categorization accuracy increases and the system appears to learn.</description><subject>ACOUSTICS</subject><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>MUSICAL INSTRUMENTS</subject><subject>PHYSICS</subject><subject>SPEECH ANALYSIS OR SYNTHESIS</subject><subject>SPEECH OR AUDIO CODING OR DECODING</subject><subject>SPEECH OR VOICE PROCESSING</subject><subject>SPEECH RECOGNITION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2015</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZBB3DA3x1w1xdHd3DVII8XAMUfBxdQzyC-ZhYE1LzClO5YXS3AzKbq4hzh66qQX58anFBYnJqXmpJfGhwUYGhqaGpiaWpmaOhsbEqQIASzAgVA</recordid><startdate>20150604</startdate><enddate>20150604</enddate><creator>BROWN DEBORAH WASHINGTON</creator><scope>EVB</scope></search><sort><creationdate>20150604</creationdate><title>AUTO-TAGGER THAT LEARNS</title><author>BROWN DEBORAH WASHINGTON</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2015154956A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2015</creationdate><topic>ACOUSTICS</topic><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>MUSICAL INSTRUMENTS</topic><topic>PHYSICS</topic><topic>SPEECH ANALYSIS OR SYNTHESIS</topic><topic>SPEECH OR AUDIO CODING OR DECODING</topic><topic>SPEECH OR VOICE PROCESSING</topic><topic>SPEECH RECOGNITION</topic><toplevel>online_resources</toplevel><creatorcontrib>BROWN DEBORAH WASHINGTON</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>BROWN DEBORAH WASHINGTON</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>AUTO-TAGGER THAT LEARNS</title><date>2015-06-04</date><risdate>2015</risdate><abstract>Examples perform context categorization by categorizing a transcription of a speech file based on the context of the subject matter of the transcription. A computer processor is configuration to provide a system that generates a normalized transcription of the speech file transcription and compares elements of the normalized transcription to elements of a context categorization model or a corpus of categorized transcriptions to determine whether the normalized transcription contains keywords of transcriptions that have previously been categorized. If the comparison yields a result indicating a specific match with a context category, the normalized transcription is assigned to the matching context category. As the number of successfully categorized transcriptions stored in the corpus increases, the more frequently the system and method examples perform successful comparisons. As a result, the context categorization accuracy increases and the system appears to learn.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | ACOUSTICS CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MUSICAL INSTRUMENTS PHYSICS SPEECH ANALYSIS OR SYNTHESIS SPEECH OR AUDIO CODING OR DECODING SPEECH OR VOICE PROCESSING SPEECH RECOGNITION |
title | AUTO-TAGGER THAT LEARNS |
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