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AUTOMATIC SPEECH TO TEXT USING ACOUSTIC MODELLING AND DEEP LEARNING

  • Unique Paper ID: 159982
  • Volume: 9
  • Issue: 12
  • PageNo: 966-970
  • Abstract:
  • Language is the most important means of communication and speech is its main medium. Many research activities are being conducted on Automatic Speech Recognition. But, ASR systems have a major drawback in their performance i.e., efficiency. Improving the efficiency in an ASR system is quite difficult. Currently, research is being carried out to the finding of the next state of the world using Hidden Markow Model (HMM). Our Study concludes that for ASR systems, Deep Learning techniques is a more suitable application, because it increases the efficiency of the whole process. We are going to represent our work on building a speaker independent, large vocabulary continuous speech recognition system for English and Hindi. Our Study concludes that for ASR systems, Deep Learning techniques is a more suitable application, because it increases the efficiency of the whole process. Based on the conclusion, we will utilize readily available language models to build an offline live Speech recognition to Text System and Translation. Using offline speech recognition toolkits like VOSK and Kaldi an offline model is developed. Argos translate an open-source library is used to translate English speech to Hindi text. Raspberry Pi 4 is used to implement the offline speech recognition module
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 12
  • PageNo: 966-970

AUTOMATIC SPEECH TO TEXT USING ACOUSTIC MODELLING AND DEEP LEARNING

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Journal no 47859

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