Implementation of Speech to Text Conversion Using Hidden Markov Model

Abstract

Deep learning is revolutionary when used to transcribe spoken language into text that computers can read with the same intent as human readers. The fundamental idea is to give intelligent systems with human language as data that may be utilized in various domains. A speech-to-text synthesizer is a piece of software that can convert an audio file into text using Digital Signal Processing (DSP) algorithms that analyze and process the speech signal in the audio file. The objective of Speech To Text (STT) is to convert audio input from a user or computer into readable text. The STT is proposed to be transformed using the Hidden Markov Model (HMM) method. The development of a speech-to-text synthesizer will be a tremendous advantage for the visually handicapped and will make reading lengthy texts much easier.