Target Speaker

Target speaker extraction (TSE) aims to isolate a specific person's voice from a noisy audio mixture, mimicking the human "cocktail party effect." Current research focuses on improving robustness to challenging conditions (e.g., overlapping speech, low signal-to-noise ratios) using various techniques, including curriculum learning, beamforming, and neural networks (e.g., convolutional recurrent networks, LSTMs) often incorporating visual cues or textual descriptions to enhance accuracy. These advancements have significant implications for improving speech recognition in noisy environments, enhancing hearing aids, and enabling more natural and effective human-computer interaction.