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Complete Text-To-Speech Application Development in Python


Complete Text-To-Speech Application Development in Python


Complete Text-To-Speech Application Development in Python Build a complete Text-to Speech application in Python from scratch using tkinter for Python GUI development.

What you'll learn

  • You will be empowered to build a project that will help you to secure a lucrative job for yourself at the end of this course!!!


  • This course requires basic knowledge of Python 3 programming
  • Basic knowledge of python GUI design would be a great plus
  • A computer system is required to follow up with the instructor


  • Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
  • Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.
  • The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.
  • A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.
  • Text-To-Speech Application System With TKinter & Python 3 teaches you the complete application of Python technology in the real life instance of speech synthesis. You will be able to implement the text to speech process from scratch following python programming principles.

Some Fundamentals of text-to-speech application system that were covered in this course are as follows:

  • Python GUI design
  • How to use voice command in python
  • You will know about Engine

Emenwa Global instructors are industry experts with years of practical, real-world experience building software at industry leading companies. They are sharing everything they know to teach thousands of students around the world, just like you, the most in-demand technical and non-technical skills (which are commonly overlooked) in the most efficient way so that you can take control of your life and unlock endless exciting new career opportunities in the world of technology, no matter your background or experience.

 Complete Text-To-Speech Application Development in Python | Udemy Course

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