Visual Interface to Speech-Cue Representation Coding

  • Ibrahim Patel Dept. of ECE B.V. Raju Inst. of Tech., Narsapur Medak,(T. S) India
  • Raghavendra Kulkarni Associate Prof. Dept. of ECE K.M.C.E&T, Devarkonda under JNT University, Hyderabad, T. S
  • Y Srinivasa Rao Prof., Department of Instrument Technology, Andhra University, Visakhapatnam, A. P
Keywords: AISR, Speech recognition, HMM, vocally disabled, communication gap, speech-processing, cue-symbol


There have being great efforts made in the development of automated Instrumentation system for speech recognition (AISR) to provide a two-way communication between deaf and vocal people. This system performance achievable with the output of current real-time speech recognition systems would be extremely poor relative to normal speech reception. An alternate application of AISR technology to aid the hearing impaired would derive cues from the acoustical speech signal that could be used to supplement speechreading. We propose a study of highly trained receivers of speech signal that indicates that nearly perfect reception of everyday connected speech materials can be achieved at near normal speaking rates. To understand the accuracy that might be achieved with automatically generated cue symbols for visual representation. The system uses (HMM) for recognition of voiced data & Euclidian distance approach for sign language. The proposed task is a complementary work to the ongoing research work for recognizing the finger movement of a vocally disabled person to speech signal called. A New communication Paradigm: “Action-To-Speech”


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How to Cite
Patel, I., Kulkarni, R., & Rao, Y. S. (2016). Visual Interface to Speech-Cue Representation Coding. European Journal of Applied Sciences, 4(5), 07.