Performance Enhancement of Speech Recognition by Using Machine Learning Techniques Specifically GAN-AE Algorithm

Abstract

Generative adversarial networks (GANs) are not very likely to have a significant role in the synthesis of speech features, thus not allowing for the creation of highly genuine representations that enhance the diversity within training datasets. Simultaneously, autoencoders (AE) serve to differentiate between genuine and synthetic speech features, while also extracting valuable insights from both domains. This symbiotic relationship between GANs and AE greatly enhances the model's ability to decode intricate patterns in speech, thereby fostering adaptability in real-world scenarios. The combination of GANs and AE in speech recognition systems transcends previous limitations, resulting in improved accuracy and reliability across a wide range of applications. Nonetheless, the fragmented nature of current approaches poses a hindrance to the progress of speech recognition boundaries, falling short of revolutionizing human-computer interaction paradigms.