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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Colección: | SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
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| 001 | 978-1-4614-1505-3 | ||
| 003 | DE-He213 | ||
| 005 | 20220117121056.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 120220s2012 xxu| s |||| 0|eng d | ||
| 020 | |a 9781461415053 |9 978-1-4614-1505-3 | ||
| 024 | 7 | |a 10.1007/978-1-4614-1505-3 |2 doi | |
| 050 | 4 | |a TK5102.9 | |
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| 100 | 1 | |a Holambe, Raghunath S. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Advances in Non-Linear Modeling for Speech Processing |h [electronic resource] / |c by Raghunath S. Holambe, Mangesh S. Deshpande. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2012. | |
| 300 | |a XIII, 102 p. 32 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, |x 2191-7388 | |
| 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. | |
| 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. | ||
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Natural language processing (Computer science). | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Signal, Speech and Image Processing . |
| 650 | 2 | 4 | |a Natural Language Processing (NLP). |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 700 | 1 | |a Deshpande, Mangesh S. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781461415060 |
| 776 | 0 | 8 | |i Printed edition: |z 9781461415046 |
| 830 | 0 | |a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, |x 2191-7388 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4614-1505-3 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||