Prosody Generation Using Back Propagation Neural Networks for Sindhi Speech Processing Applications

Abstract
Analysis and synthesis of speech to be automated still require more research efforts in general and for the development of speech processing applications based on Arabic Script like Sindh Textto-Speech in particular. To achieve the required results from the speech processing applications prosodic features must be exercised extremely as the prosody is highly linked with the information of sounds having different characteristics like linguistic rules, complications and variations of expressions.

Objectives: This study aims to generate and analyze the prosodic information specifically pitch and duration from the recorded Sindhi sounds using the back propagation neural network.

Methods: Two methods are used to obtain the prosodic information of Sindhi sounds, PRAAT speech analyser is used to obtain the results and for the validation a back propagation neural network model is implemented. From the four districts of Sindh 228 speakers were chosen and the sound of different descriptive sentences was recorded for the experiments.

Finding: After the experiments with a neural network model with multiple layers on the collected sound, 98.8% a highly acceptable level of accuracy achieved at the 18th epoch among the 100 epochs.

Application and improvements: The generated Sindhi prosodic information and adopted research methodology will be supportive to the scholars of Sindhi speech processing applications. This research work can be considered as the first step as no work for generating Sindhi prosody is found yet.

Keywords: Sindhi Recorded Sounds, Pitch, Duration, Speech Analysis, Prosody Generation