A Modern Approach for Speech to Text and Text to Speech Conversion Application Using Machine Learning Techniques

Abstract

Over the past few decades, designers are considering a range of applications ranging from mobile communications to automatic machine learning. Speeches are less commonly used in the electronic and computer field due to the complexity and variety of signals and sounds. By the use of modern algorithms and methods, speech signals are processed to recognize text. In this project, we will build a online speech to text engine. The program receives speech during the run through the microphone and uses sample speech to recognize the text. The known text can be saved to a file. This is being developed on Java platform using the eclipse workbench. Our speech-to-text program directly gets and converts speech into text. It can add other great applications, giving users a different choice of data input. A text-to-speech system can also improve accessibility of the system by providing data access options for users who are blind, deaf or disabled. Voice SMS application allows the user to record and convert spoken messages into an text message. The user can send messages to the phone number which is entered. Speech recognition is done through the Internet, connecting to Google's server. The application is based on input messages in English. Speech recognition uses a technique based on hidden Markov models (HMM - Hidden Markov Model). It is currently the most effective and flexible method of speech recognition.