SOPC-Based Speech-to-Text Conversion 83 Second Prize SOPC-Based Speech-to-Text Conversion Institution: National Institute of Technology, Trichy Participants: M.T. Bala Murugan and M. Balaji Instructor: Dr. B. Venkataramani Design Introduction For the past several decades, designers have processed speech for a wide variety of applications ranging from mobile communications to automatic reading machines. Speech recognition reduces the overhead caused by alternate communication methods. Speech has not been used much in the field of electronics and computers due to the complexity and variety of speech signals and sounds. However, with modern processes, algorithms, and methods we can process speech signals easily and recognize the text. Objective In our project, we developed an on-line speech-to-text engine, implemented as a system-on-a- programmable-chip (SOPC) solution. The system acquires speech at run time through a microphone and processes the sampled speech to recognize the uttered text. We used the hidden Markov model (HMM) for speech recognition, which converts the speech to text. The recognized text can be stored in a file on a PC that connects to an FPGA on a development board using a standard RS-232 serial cable. Our speech-to-text system directly acquires and converts speech to text. It can supplement other larger systems, giving users a different choice for data entry. A speech-to-text system can also improve system accessibility by providing data entry options for blind, deaf, or physically handicapped users. Project Outline The project implements a speech-to-text system using isolated word recognition with a vocabulary of ten words (digits 0 to 9) and statistical modeling (HMM) for machine speech recognition. In the training phase, the uttered digits are recorded using 16-bit pulse code modulation (PCM) with a sampling rate of 8 KHz and saved as a wave file using sound recorder software. We use the MATLAB software’s wavread command to convert the .wav files to speech samples.
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At-A-Glance Mill Services
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