Time Domain Speech Enhancement

Time-domain speech enhancement aims to improve the quality and intelligibility of speech signals by directly processing the waveform, rather than relying on frequency-domain representations. Current research emphasizes the development of novel neural network architectures, including conformers and diffusion models, often integrated into generative adversarial networks (GANs) or combined with deterministic modules for enhanced performance. These advancements are driven by the need for robust speech enhancement in challenging acoustic environments, particularly for applications like voice-over-internet-protocol (VoIP) communication and improving the accuracy of automatic speech recognition systems. The resulting improvements in speech quality and robustness have significant implications for various fields, including telecommunications and human-computer interaction.