Robust adaptation to non-native accents in automatic speech recognition
Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are descri...
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Collection: | Lecture notes in computer science. Lecture notes in artificial intelligence
2560 |
| Sujets: | |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Robust adaptation to non-native accents in automatic speech recognition, Silke Goronzy, Berlin, Springer, 2002, 1 vol. (XI-144 p.), Lecture notes in computer science, 3-540-00325-8 • Robust Adaptation to Non-Native Accents in Automatic Speech Recognition, Texte imprimé, 9783662205549 |
Table des matières:
- ASR:AnOverview
- Pre-processing of the Speech Data
- Stochastic Modelling of Speech
- Knowledge Bases of an ASR System
- Speaker Adaptation
- Confidence Measures
- Pronunciation Adaptation
- Future Work
- Summary
- Databases and Experimental Settings
- MLLR Results
- Phoneme Inventory.