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170203 ||| eng |
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|a 9783319492209
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|a Rao, K. Sreenivasa
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| 245 |
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|a Speech Recognition Using Articulatory and Excitation Source Features
|h Elektronische Ressource
|c by K. Sreenivasa Rao, Manjunath K E.
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| 250 |
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|a 1st ed. 2017
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2017, 2017
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| 300 |
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|a XI, 92 p. 23 illus., 4 illus. in color
|b online resource
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| 505 |
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|a Introduction -- Literature Review -- Articulatory Features for Phone Recognition -- Excitation Source Features for Phone Recognition -- Articulatory and Excitation Source Features for Speech Recognition in Read, Extempore and Conversation Modes -- Conclusion -- Appendix A: MFCC Features -- Appendix B: Pattern Recognition Models
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| 653 |
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|a Computational Linguistics
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| 653 |
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|a Computational linguistics
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Natural Language Processing (NLP)
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| 653 |
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|a Signal processing
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| 653 |
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|a Natural language processing (Computer science)
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| 700 |
1 |
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|a K E, Manjunath
|e [author]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning
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| 028 |
5 |
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|a 10.1007/978-3-319-49220-9
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| 856 |
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|u https://doi.org/10.1007/978-3-319-49220-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 621,382
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| 520 |
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|a This book discusses the contribution of articulatory and excitation source information in discriminating sound units. The authors focus on excitation source component of speech -- and the dynamics of various articulators during speech production -- for enhancement of speech recognition (SR) performance. Speech recognition is analyzed for read, extempore, and conversation modes of speech. Five groups of articulatory features (AFs) are explored for speech recognition, in addition to conventional spectral features. Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features. The authors close by discussing various combinations of spectral, articulatory and source features, and the desired models to enhance the performance of SR systems
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