DocumentCode
584497
Title
Comparative Study of Phoneme Recognition Techniques
Author
Kshirsagar, Abhijeet ; Dighe, Aditi ; Nagar, Kartik ; Patidar, M.
Author_Institution
Sch. Of Comput. Sci. & IT, DAVV Indore, Indore, India
fYear
2012
fDate
23-25 Nov. 2012
Firstpage
98
Lastpage
103
Abstract
Automatic Speech Recognition is the most popular and demanding field in the research area. For most of the real world applications, Phoneme recognition is important for successful development of ASR. This Paper gives an overview of the techniques and systems for the Phoneme recognition based on three categories-Vector Quantization, HMM, Neural Network followed by comparative study of different techniques. This paper helps in selecting the appropriate technique along with its feature description. Also gives the generalized approach of the phoneme recognition technique to understand their working. This paper concludes with the decision that the present phoneme recognition techniques work better for isolated words then continues speech.
Keywords
hidden Markov models; neural nets; speech recognition; ASR; HMM; automatic speech recognition; feature description; hidden Markov model; neural network; phoneme recognition technique; vector quantization; Educational institutions; Hidden Markov models; Mel frequency cepstral coefficient; Neural networks; Speech; Speech recognition; Vector quantization; Automatic Speech Recognition (ASR); HMM; Neural Network; Phoneme Recognition; Vector Quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technology (ICCCT), 2012 Third International Conference on
Conference_Location
Allahabad
Print_ISBN
978-1-4673-3149-4
Type
conf
DOI
10.1109/ICCCT.2012.28
Filename
6394676
Link To Document