Effect of High-Energy Voiced Speech Segments and Speaker Gender on Shouted Speech Detection

Shouted speech detection is an essential preprocessing task in many conventional speech processing systems. Mostly, shouted speech has been studied in terms of the characterization of vocal tract and excitation source features. Previous works have also established the significance of voiced segments in shouted speech detection. This work posits that a significant emphasis is given to a portion of the voiced segments during shouted speech production. These emphasized voiced regions have significant energy. This work analyzes the effect of high-energy voiced segments on shouted speech detection. Moreover, fundamental frequency is a crucial characteristic of both shouted speech and speaker gender. Authors believe that gender has a significant effect on shouted speech detection. Therefore, the present work also studies the impact of gender on the current task. The classification between normal and shouted speech is performed using a DNN based classifier. A statistical significance test of the features extracted from high-energy voiced segments is also performed. The results support the claim that high-energy voiced segments carry highly discriminating information. Additionally, classification results of gender experiments show that gender has a notable effect on shouted speech detection.