Advances in non-linear modeling for speech processing
Advances in Non-Linear Modeling for Speech Processing includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition. Non-linear aeroacoustic modeling approach is used to estimate the important fine-structure speech events, which are no...
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| Auteurs principaux: | , |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado em: |
New York, NY :
Springer New York
2012.
Cham : Springer Nature |
| Colecção: | SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning
|
| Acesso em linha: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Advances in Non-Linear Modeling for Speech Processing, Texte imprimé, 9781461415046 • Advances in Non-Linear Modeling for Speech Processing, Texte imprimé, 9781461415060 |
| LEADER | 04002nam a22003497a 4500 | ||
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| 001 | 974017 | ||
| 008 | 120309s2012 xx ||| |||| 00| 0 eng d | ||
| 009 | PPN159082005 | ||
| 020 | |a 9781461415053 | ||
| 020 | |a 9781461415053 | ||
| 041 | 0 | |a eng | |
| 082 | |a 621.382 | ||
| 100 | 1 | |a Holambe, Raghunath S. | |
| 245 | 1 | 0 | |a Advances in non-linear modeling for speech processing |c by Raghunath S. Holambe, Mangesh S. Deshpande. |
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c 2012. | ||
| 490 | 0 | |a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning |x 2191-7388 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a From the Contents: Speech production mechanism -- Linear speech production model -- Nonlinearity in speech production -- Nonlinear dynamic system model -- Speech perception mechanism -- Summary -- Autoregressive models -- Linear autoregressive model -- Nonlinear autoregressive model -- Nonlinear measurement and modeling using Teager energy operator -- Teager energy operator (TEO) -- Vocal tract aeroacoustic flow -- Energy measurement -- Energy separation -- Noise suppression using TEO. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Advances in Non-Linear Modeling for Speech Processing includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition. Non-linear aeroacoustic modeling approach is used to estimate the important fine-structure speech events, which are not revealed by the short time Fourier transform (STFT). This aeroacostic modeling approach provides the impetus for the high resolution Teager energy operator (TEO). This operator is characterized by a time resolution that can track rapid signal energy changes within a glottal cycle. The cepstral features like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the magnitude spectrum of the speech frame and the phase spectra is neglected. To overcome the problem of neglecting the phase spectra, the speech production system can be represented as an amplitude modulation-frequency modulation (AM-FM) model. To demodulate the speech signal, to estimation the amplitude envelope and instantaneous frequency components, the energy separation algorithm (ESA) and the Hilbert transform demodulation (HTD) algorithm are discussed. Different features derived using above non-linear modeling techniques are used to develop a speaker identification system. Finally, it is shown that, the fusion of speech production and speech perception mechanisms can lead to a robust feature set. | ||
| 700 | 1 | |a Deshpande, Mangesh S. |4 aut | |
| 776 | 0 | |t Advances in Non-Linear Modeling for Speech Processing |b Texte imprimé |z 9781461415046 | |
| 776 | 0 | |t Advances in Non-Linear Modeling for Speech Processing |b Texte imprimé |z 9781461415060 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-1-4614-1505-3 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-8LMRSFD2-T |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:750605170 |u https://ezproxy.univ-orleans.fr/login?url=https://dx.doi.org/10.1007/978-1-4614-1505-3 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:75396516X |u https://ezproxy.insa-cvl.fr/login?qurl=https://dx.doi.org/10.1007/978-1-4614-1505-3 |z Accès INSA CVL | |
| 997 | |0 974017 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||