00743nam0 2200265 450 00000832420071002145445.0978-88-420-8136-420071002d2006----km-y0itay50------baitaITy-------001yyOltre lo StatoSabino CasseseRomaLaterzac2006196 p.21 cmSagittari Laterza1522001Sagittari LaterzaDirittoUnificazione internazionale340.1120Cassese,Sabino7706ITUNIPARTHENOPE20071002RICAUNIMARC000008324340-O/139838NAVA12007Oltre lo Stato716093UNIPARTHENOPE01174nam0-22004211i-450-99000121731040332120080521163438.0000121731FED01000121731(Aleph)000121731FED0100012173120001205d1965----km-y0itay50------baengGBy-------001yy<<The >>mathematical theory of linear systemsB.M. Brown2nd ed.LondonChapman and Hall1965VIII, 279 p.22 cmAutomation and control engineering seriesScience paperbacks14Teoria dei sistemi003.74620.7Brown,B. M.<Basil Montgomery ;1912- >40588ITUNINARICAUNIMARCBK9900012173104033219-I-206363MA19-I-216366MA102 49 A 91674FINBN08 LL 3669 C.E.DINEDMA1FINBNDINED93AXX93C05Mathematical theory of linear systems342877UNINA03974nam 22006375 450 991015938630332120251116170928.03-319-49220-910.1007/978-3-319-49220-9(CKB)3710000001019198(DE-He213)978-3-319-49220-9(MiAaPQ)EBC4786382(PPN)198341083(EXLCZ)99371000000101919820170111d2017 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierSpeech Recognition Using Articulatory and Excitation Source Features /by K. Sreenivasa Rao, Manjunath K E1st ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (XI, 92 p. 23 illus., 4 illus. in color.)SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737X3-319-49219-5 Includes bibliographical references at the end of each chapters.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.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.SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737XSignal processingImage processingSpeech processing systemsNatural language processing (Computer science)Computational linguisticsSignal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Natural Language Processing (NLP)https://scigraph.springernature.com/ontologies/product-market-codes/I21040Computational Linguisticshttps://scigraph.springernature.com/ontologies/product-market-codes/N22000Signal processing.Image processing.Speech processing systems.Natural language processing (Computer science)Computational linguistics.Signal, Image and Speech Processing.Natural Language Processing (NLP).Computational Linguistics.152.15Rao K. Sreenivasa(Krothapalli Sreenivasa),authttp://id.loc.gov/vocabulary/relators/aut0K E Manjunathauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910159386303321Speech Recognition Using Articulatory and Excitation Source Features4003483UNINA