The field of speech processing is moving towards the development of more efficient and effective algorithms for speech enhancement and translation. Researchers are exploring the use of neural networks and attention mechanisms to improve the quality of speech enhancement in noisy environments. Additionally, there is a growing interest in developing systems that can handle multichannel audio and real-time processing. Noteworthy papers in this area include NeuralPMWF, which uses a low-latency neural network to control a parameterized multi-channel Wiener filter, resulting in significantly better perceptual and objective speech enhancement. MeMo is another notable work, which proposes a novel framework for real-time audio-visual speaker extraction under impaired visual conditions, achieving SI-SNR improvements of at least 2 dB over the corresponding baseline.