Automatically optimize text for spoken word audio readability and naturalness.

This involved sentence restructuring, paragraph segmentation, and intelligent word choice adjustments for smoother audio delivery. Existing pre-trained models for summarization and text optimization for spoken word audio were insufficient, as they were not designed for the specific nuances of optimizing text for audio. They often produced overly unnatural sounding speech from text & condensed summaries that lacked the natural flow and rhythm required for spoken language. We extensively tested various TTS engines, both open-source and commercial, to assess their strengths and weaknesses in terms of accuracy, naturalness, customization options, and processing speed. Design - We designed a modular pipeline with separate stages for text pre-processing, content classification, text transformation, and speech synthesis. - Technical Work: We implemented the pipeline using Python, and several LLM libraries such as Langchain, etc. We collected and annotated a dataset of thousands of text samples across various content types. - Observations: Initial results were promising, showing improvements in audio naturalness and readability. However, the model struggled with complex sentence structures, idiomatic expressions, and customer specific branding requests. - Analysis & Conclusion: We concluded that our approach had further potential but required further refinement of the text optimization algorithms and expansion of the training dataset. We have brought this to commercialization and are generating revenue from this project now