Visionary Audio Hub using Machine Learning

Visionary Audio Hub using Machine Learning

Sujith Kumar S, Santhosh B, Guruakash SM, Dr Murugan K

Bannari Amman Institute of Technology

Sathyamangalam, India

Abstract— Image-to-text-to-speech conversion, powered by machine learning, is an emerging field to transform how we access and engage with information. By integrating optical character recognition (OCR) and text-to-speech (TTS) technologies, machine learning enables the extraction of text from images and its conversion into speech with greater accuracy and efficiency than ever before. This technology holds immense potential to enhance accessibility for a broad spectrum of users, including those with visual impairments, students, tourists, researchers, and musicians. For instance, individuals with visual impairments can use image-to-text-to-speech conversion to access scanned textbooks and course materials in speech format, facilitating easier comprehension and study. Similarly, tourists can leverage this technology to translate foreign language signs and text into speech, aiding navigation in unfamiliar environments. Researchers can benefit from image-to-text-to-speech conversion by extracting data from scientific papers and documents, simplifying analysis and synthesis processes. Moreover, musicians can explore new creative avenues by converting text to speech and manipulating the audio output to innovate musical compositions. Machine learning algorithms play a crucial role in improving the quality and naturalness of synthesized speech in these systems. By considering factors such as language, accent, and prosody, machine learning algorithms generate speech that closely resembles human speech patterns, enhancing clarity and understanding.

Keywords—   Image-to-text-to-speech conversion, Machine learning, Optical character recognition (OCR), Text-to-speech (TTS) technologies, Accessibility, Naturalness of synthesized speech.