Optimizing Speech Synthesis for Efficient Text-to-Speech Conversion with Enhanced Robustness and Resource Efficiency - Sandhu , Mukta (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Speech synthesis is conversion of text to speech but it become very challenging when there is background noise. Additionally, it is very time-consuming, has high cost, and more power-consuming. To overcome these issues design a Butterfly-based Convolutional Neural System (BbCNS). Initially, the input text of certain users was collected and trained into the system and the preprocessing is utilized for removing the errors present in the dataset and preparing the text data for a specific context. Additionally, data normalization is employed to transfer the text into canonical and consistent form. Additionally, linguistic analysis is used to understand the content of the text and to identify the constituent morphemes of each word. Furthermore, Prosodic prominence prediction can be predicted from written language. Finally, the waveform is generated for converting text into speech. At last, the outcomes that are gained from the model that is designed are validated using other prevailing models with respect to accuracy, sensitivity, specificity, precision, and computation time.