1.

Record Nr.

UNISA990000233520203316

Autore

Prague topological Symposium : 5. : 1981

Titolo

General topology and its relationsto modern analysis and algebra V : proceedings of the fifth Prague topological symposium 1981 / J. Novák editor

Pubbl/distr/stampa

Berlin : Heldermann Verlag, copyr. 1983

ISBN

3-88538-003-X

Descrizione fisica

728 p. : ill. ; 24 cm

Collana

Sigma series in pure mathematics ; 3

Disciplina

514

Collocazione

514 PRA

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



2.

Record Nr.

UNINA9910159386303321

Autore

Rao K. Sreenivasa (Krothapalli Sreenivasa)

Titolo

Speech Recognition Using Articulatory and Excitation Source Features / / by K. Sreenivasa Rao, Manjunath K E

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017

ISBN

3-319-49220-9

Edizione

[1st ed. 2017.]

Descrizione fisica

1 online resource (XI, 92 p. 23 illus., 4 illus. in color.)

Collana

SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, , 2191-737X

Disciplina

152.15

Soggetti

Signal processing

Image processing

Speech processing systems

Natural language processing (Computer science)

Computational linguistics

Signal, Image and Speech Processing

Natural Language Processing (NLP)

Computational Linguistics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references at the end of each chapters.

Nota di contenuto

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.

Sommario/riassunto

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.