ICA-Based Speech Features in the Frequency Domain
We apply the technique of independent component analysis to Fourier power coefficients of speech signal frames for a blind detection of basic vectors (sources). A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminate...
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| creator | Kasprzak, Włodzimierz Okazaki, Adam F. Kowalski, Adam B. |
| description | We apply the technique of independent component analysis to Fourier power coefficients of speech signal frames for a blind detection of basic vectors (sources). A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminated. The mixing coefficients for such sources are then determined for every speech sample. We compare our features with the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition. |
| doi_str_mv | 10.1007/11679363_76 |
| format | Book Chapter |
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A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminated. The mixing coefficients for such sources are then determined for every speech sample. We compare our features with the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540326308</identifier><identifier>ISBN: 9783540326304</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540326316</identifier><identifier>EISBN: 9783540326311</identifier><identifier>DOI: 10.1007/11679363_76</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Automatic Speech Recognition ; Computer science; control theory; systems ; Exact sciences and technology ; Independent Component Analysis ; Information, signal and communications theory ; Signal processing ; Speaker Recognition ; Speech and sound recognition and synthesis. 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A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminated. The mixing coefficients for such sources are then determined for every speech sample. We compare our features with the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Automatic Speech Recognition</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Independent Component Analysis</subject><subject>Information, signal and communications theory</subject><subject>Signal processing</subject><subject>Speaker Recognition</subject><subject>Speech and sound recognition and synthesis. Linguistics</subject><subject>Speech Recognition</subject><subject>Speech Sample</subject><subject>Telecommunications and information theory</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540326308</isbn><isbn>9783540326304</isbn><isbn>3540326316</isbn><isbn>9783540326311</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><recordid>eNpNkL1OwzAURs2fRFuYeIEsDAyBe31dOx6hJYBUiQGYLdu5oYE2DXE79O0pKhJM33COzvAJcYFwjQDmBlEbS5qc0QdiSGMFJDWhPhQD1Ig5kbJHfwCKYzEAAplbo-hUDFP6AABprBwIfJrc5nc-cZW9dMxxnpXs15ueU9a02XrOWdnz14bbuM2mq6Vv2jNxUvtF4vPfHYm38v518pjPnh92sVneybFe56q2OAZT-VoFAmOrqggBishFrJQ2Ra3AkNVSchV0sF5RrL2PAY3WFDjSSFzuu51P0S_q3rexSa7rm6Xvtw6tRTCSdt7V3ks71L5z78Jq9Zkcgvt5y_17i74B66FV9w</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Kasprzak, Włodzimierz</creator><creator>Okazaki, Adam F.</creator><creator>Kowalski, Adam B.</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>ICA-Based Speech Features in the Frequency Domain</title><author>Kasprzak, Włodzimierz ; Okazaki, Adam F. ; Kowalski, Adam B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p256t-4f91507daf4b3079dd8bb08ce8cd4678f40739622edb6b9a43cfaacb17663bec3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Automatic Speech Recognition</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Independent Component Analysis</topic><topic>Information, signal and communications theory</topic><topic>Signal processing</topic><topic>Speaker Recognition</topic><topic>Speech and sound recognition and synthesis. Linguistics</topic><topic>Speech Recognition</topic><topic>Speech Sample</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kasprzak, Włodzimierz</creatorcontrib><creatorcontrib>Okazaki, Adam F.</creatorcontrib><creatorcontrib>Kowalski, Adam B.</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kasprzak, Włodzimierz</au><au>Okazaki, Adam F.</au><au>Kowalski, Adam B.</au><au>Erdogmus, Deniz</au><au>Haykin, Simon</au><au>Príncipe, José C.</au><au>Rosca, Justinian</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>ICA-Based Speech Features in the Frequency Domain</atitle><btitle>Independent Component Analysis and Blind Signal Separation</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><spage>609</spage><epage>616</epage><pages>609-616</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540326308</isbn><isbn>9783540326304</isbn><eisbn>3540326316</eisbn><eisbn>9783540326311</eisbn><abstract>We apply the technique of independent component analysis to Fourier power coefficients of speech signal frames for a blind detection of basic vectors (sources). A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminated. The mixing coefficients for such sources are then determined for every speech sample. We compare our features with the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11679363_76</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Independent Component Analysis and Blind Signal Separation, 2006, p.609-616 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19910723 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Automatic Speech Recognition Computer science control theory systems Exact sciences and technology Independent Component Analysis Information, signal and communications theory Signal processing Speaker Recognition Speech and sound recognition and synthesis. Linguistics Speech Recognition Speech Sample Telecommunications and information theory |
| title | ICA-Based Speech Features in the Frequency Domain |
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