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Speech recognition or speech to text conversion has rapidly gained a lot of interest by large organizations in order to ease the process of human to machine communication. Optimization of the speech recognition process is of utmost importance, due to the fact that real-time users want to perform actions based on the input speech given by them, and these actions sometime define the lifestyle of the users and thus the process of speech to text conversion should be carried out accurately. Here`s the plan to improve the accuracy of this process with the help of natural language processing and speech analysis. Some existing speech recognition softwares of Google, Amazon, and Microsoft tend to have an accuracy of more than 90% in real time speech detection. This system combines the speech recognition approach used by these softwares and joined with language processing to improve the overall accuracy of the process with the help of phonetic analysis. Proposed Phonetic Model supports multi-lingual speech recognition and observed that the accuracy of this system is 90% for Hindi and English speech to text recognition. The Hindi WordNet database provided by IIT Mumbai used in this research work for Hindi speech to text conversion.

Implementation of Efficient Speech Recognition System on Mobile Device for Hindi and English Language