• 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