Speech Enhancement Model

Speech enhancement models aim to improve the clarity of speech signals degraded by noise and reverberation, primarily for applications like hearing aids and voice assistants. Current research emphasizes developing models with ultra-low latency for real-time applications, often employing techniques like asymmetric windows, adaptive filterbanks, and novel architectures such as the Mamba network, while also exploring the use of self-supervised speech representations to improve training and reduce model size. This field is crucial for improving the accessibility and usability of speech technologies, impacting areas such as hearing healthcare, communication systems, and human-computer interaction.