Frequency and wavelet filtering for robust speech recognition
Identifieur interne :
000727 ( PascalFrancis/Corpus );
précédent :
000726;
suivant :
000728
Frequency and wavelet filtering for robust speech recognition
Auteurs : Murat Deviren ;
Khalid DaoudiSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 2003.
RBID : Pascal:04-0130632
Descripteurs français
English descriptors
Abstract
Mel-frequency cepstral coefficients (MFCC) are the most widely used features in current speech recognition systems. However, they have a poor physical interpretation and they do not lie in the frequency domain. Frequency filtering (FF) is a technique that has been recently developed to design frequency-localized speech features that perform similar to MFCC in terms of recognition performances. Motivated by our desire to build time-frequency speech models, we wanted to use the FF technique as front-end. However, when evaluating FF on the Aurora-3 database we found some discrepancies in the highly mismatch case. This led us to put FF in another perspective: the wavelet transform. By doing so, we were able to explain the discrepancies and to achieve significant improvements in recognition in the highly mismatch case.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A11 | 01 | 1 | | @1 DEVIREN (Murat) |
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| A43 | 01 | | | @1 INIST @2 16343 @5 354000117807160540 |
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| C01 | 01 | | ENG | @0 Mel-frequency cepstral coefficients (MFCC) are the most widely used features in current speech recognition systems. However, they have a poor physical interpretation and they do not lie in the frequency domain. Frequency filtering (FF) is a technique that has been recently developed to design frequency-localized speech features that perform similar to MFCC in terms of recognition performances. Motivated by our desire to build time-frequency speech models, we wanted to use the FF technique as front-end. However, when evaluating FF on the Aurora-3 database we found some discrepancies in the highly mismatch case. This led us to put FF in another perspective: the wavelet transform. By doing so, we were able to explain the discrepancies and to achieve significant improvements in recognition in the highly mismatch case. |
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Format Inist (serveur)
| NO : | PASCAL 04-0130632 INIST |
|---|
| ET : | Frequency and wavelet filtering for robust speech recognition |
|---|
| AU : | DEVIREN (Murat); DAOUDI (Khalid); KAYNAK (Okyay); ALPAYDIN (Ethem); OJA (Erkki); LEI XU |
|---|
| AF : | INRIA-LORIA (Speech Group) B.P. 101/54602 Villers les Nancy/France (1 aut., 2 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2003; Vol. 2714; Pp. 452-460; Bibl. 11 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | Mel-frequency cepstral coefficients (MFCC) are the most widely used features in current speech recognition systems. However, they have a poor physical interpretation and they do not lie in the frequency domain. Frequency filtering (FF) is a technique that has been recently developed to design frequency-localized speech features that perform similar to MFCC in terms of recognition performances. Motivated by our desire to build time-frequency speech models, we wanted to use the FF technique as front-end. However, when evaluating FF on the Aurora-3 database we found some discrepancies in the highly mismatch case. This led us to put FF in another perspective: the wavelet transform. By doing so, we were able to explain the discrepancies and to achieve significant improvements in recognition in the highly mismatch case. |
|---|
| CC : | 001D02C04; 001D04A05B |
|---|
| FD : | Intelligence artificielle; Interface utilisateur; Base donnée; Reconnaissance parole; Reconnaissance automatique; Filtrage fréquence; Réponse temporelle; Cepstre; Transformation ondelette; 4372 |
|---|
| ED : | Artificial intelligence; User interface; Database; Speech recognition; Automatic recognition; Frequency filtering; Time response; Cepstrum; Wavelet transformation |
|---|
| SD : | Inteligencia artificial; Interfase usuario; Base dato; Reconocimiento voz; Reconocimiento automático; Filtrado frecuencia; Respuesta temporal; Transformación ondita |
|---|
| LO : | INIST-16343.354000117807160540 |
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| ID : | 04-0130632 |
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Links to Exploration step
Pascal:04-0130632
Le document en format XML
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<EA>Mel-frequency cepstral coefficients (MFCC) are the most widely used features in current speech recognition systems. However, they have a poor physical interpretation and they do not lie in the frequency domain. Frequency filtering (FF) is a technique that has been recently developed to design frequency-localized speech features that perform similar to MFCC in terms of recognition performances. Motivated by our desire to build time-frequency speech models, we wanted to use the FF technique as front-end. However, when evaluating FF on the Aurora-3 database we found some discrepancies in the highly mismatch case. This led us to put FF in another perspective: the wavelet transform. By doing so, we were able to explain the discrepancies and to achieve significant improvements in recognition in the highly mismatch case.</EA>
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