Time–domain non-linear feature parameter for consonant classification
Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Thasleema, T. M. [verfasserIn] Prajith, P. [verfasserIn] Narayanan, N. K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of speech technology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 15(2012), 2 vom: 08. März, Seite 227-239 |
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| Übergeordnetes Werk: |
volume:15 ; year:2012 ; number:2 ; day:08 ; month:03 ; pages:227-239 |
| Links: |
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| DOI / URN: |
10.1007/s10772-012-9136-6 |
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| Katalog-ID: |
SPR013131516 |
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| 520 | |a Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. | ||
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| 650 | 4 | |a Artificial neural network |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Prajith, P. |e verfasserin |4 aut | |
| 700 | 1 | |a Narayanan, N. K. |e verfasserin |4 aut | |
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10.1007/s10772-012-9136-6 doi (DE-627)SPR013131516 (SPR)s10772-012-9136-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Thasleema, T. M. verfasserin aut Time–domain non-linear feature parameter for consonant classification 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. Reconstructed state space (dpeaa)DE-He213 State space map (dpeaa)DE-He213 State space point distribution (dpeaa)DE-He213 -Nearest neighborhood (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Prajith, P. verfasserin aut Narayanan, N. K. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 15(2012), 2 vom: 08. März, Seite 227-239 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:15 year:2012 number:2 day:08 month:03 pages:227-239 https://dx.doi.org/10.1007/s10772-012-9136-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 17.00 ASE AR 15 2012 2 08 03 227-239 |
| spelling |
10.1007/s10772-012-9136-6 doi (DE-627)SPR013131516 (SPR)s10772-012-9136-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Thasleema, T. M. verfasserin aut Time–domain non-linear feature parameter for consonant classification 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. Reconstructed state space (dpeaa)DE-He213 State space map (dpeaa)DE-He213 State space point distribution (dpeaa)DE-He213 -Nearest neighborhood (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Prajith, P. verfasserin aut Narayanan, N. K. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 15(2012), 2 vom: 08. März, Seite 227-239 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:15 year:2012 number:2 day:08 month:03 pages:227-239 https://dx.doi.org/10.1007/s10772-012-9136-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 17.00 ASE AR 15 2012 2 08 03 227-239 |
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10.1007/s10772-012-9136-6 doi (DE-627)SPR013131516 (SPR)s10772-012-9136-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Thasleema, T. M. verfasserin aut Time–domain non-linear feature parameter for consonant classification 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. Reconstructed state space (dpeaa)DE-He213 State space map (dpeaa)DE-He213 State space point distribution (dpeaa)DE-He213 -Nearest neighborhood (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Prajith, P. verfasserin aut Narayanan, N. K. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 15(2012), 2 vom: 08. März, Seite 227-239 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:15 year:2012 number:2 day:08 month:03 pages:227-239 https://dx.doi.org/10.1007/s10772-012-9136-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 17.00 ASE AR 15 2012 2 08 03 227-239 |
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10.1007/s10772-012-9136-6 doi (DE-627)SPR013131516 (SPR)s10772-012-9136-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Thasleema, T. M. verfasserin aut Time–domain non-linear feature parameter for consonant classification 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. Reconstructed state space (dpeaa)DE-He213 State space map (dpeaa)DE-He213 State space point distribution (dpeaa)DE-He213 -Nearest neighborhood (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Prajith, P. verfasserin aut Narayanan, N. K. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 15(2012), 2 vom: 08. März, Seite 227-239 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:15 year:2012 number:2 day:08 month:03 pages:227-239 https://dx.doi.org/10.1007/s10772-012-9136-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 17.00 ASE AR 15 2012 2 08 03 227-239 |
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10.1007/s10772-012-9136-6 doi (DE-627)SPR013131516 (SPR)s10772-012-9136-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Thasleema, T. M. verfasserin aut Time–domain non-linear feature parameter for consonant classification 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. Reconstructed state space (dpeaa)DE-He213 State space map (dpeaa)DE-He213 State space point distribution (dpeaa)DE-He213 -Nearest neighborhood (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Prajith, P. verfasserin aut Narayanan, N. K. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 15(2012), 2 vom: 08. März, Seite 227-239 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:15 year:2012 number:2 day:08 month:03 pages:227-239 https://dx.doi.org/10.1007/s10772-012-9136-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 17.00 ASE AR 15 2012 2 08 03 227-239 |
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ddc 400 bkl 17.00 misc Reconstructed state space misc State space map misc State space point distribution misc -Nearest neighborhood misc Artificial neural network |
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time–domain non-linear feature parameter for consonant classification |
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Time–domain non-linear feature parameter for consonant classification |
| abstract |
Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. |
| abstractGer |
Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. |
| abstract_unstemmed |
Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR013131516</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111001147.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10772-012-9136-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR013131516</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10772-012-9136-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">400</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">17.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Thasleema, T. M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Time–domain non-linear feature parameter for consonant classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reconstructed state space</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">State space map</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">State space point distribution</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">-Nearest neighborhood</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial neural network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Prajith, P.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Narayanan, N. K.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of speech technology</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995</subfield><subfield code="g">15(2012), 2 vom: 08. März, Seite 227-239</subfield><subfield code="w">(DE-627)27134895X</subfield><subfield code="w">(DE-600)1479775-6</subfield><subfield code="x">1572-8110</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2012</subfield><subfield code="g">number:2</subfield><subfield code="g">day:08</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:227-239</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10772-012-9136-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" 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