DocumentCode
3544712
Title
Robust Automatic Speech Recognition System: Hmm Versus Sparse
Author
Mohammed, Musfir ; Edet, B.K. ; Carrol, X.C. ; Yasif, K.A. ; Rahamathulla ; Supriya, V.
Author_Institution
Dept. of Electron. & Commun. Eng., MES Coll. of Eng., Kuttippuram, India
fYear
2012
fDate
8-10 Feb. 2012
Firstpage
339
Lastpage
342
Abstract
Speech Recognition has been an ever growing and challenging area for the researchers as well as the industry. It is defined in the computer domain as the ability to ability of computer systems to accept spoken words in audio format - such as. wav or raw and perform tasks accordingly. Despite the wide diffusion of commercial applications most of the research works are done in either English, Arabic or Mandarin and the technology underlying is known to only a few laboratories[1]. Thus the development of such a system is still on the primitive stage towards the local Indian languages. This paper discusses regarding an attempt to develop a digit recognition system for Malayalam language using the HMM Toolkit (HTK). Application of Sparse Imputation to recognition algorithm is discussed so as to increase the robustness rather than the conventional Hidden Markov Model technique.
Keywords
hidden Markov models; speech recognition; Arabic language; English language; HMM toolkit; Indian language; Mandarin language; automatic speech recognition system; hidden Markov model; sparse imputation; spoken words; Hidden Markov models; Mel frequency cepstral coefficient; Speech; Speech recognition; Training; Vectors; Vocabulary; Automatic Speech Recognition; BFCC; HMM; MFCC; RPLP; Sparse;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems, Modelling and Simulation (ISMS), 2012 Third International Conference on
Conference_Location
Kota Kinabalu
Print_ISBN
978-1-4673-0886-1
Type
conf
DOI
10.1109/ISMS.2012.66
Filename
6169725
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