We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference. It firstly learns two separate spaces of speaker-knowledge and speech-stimuli based on a shared feature space, where a new block structure is designed as the building block for all spaces, and then cooperatively solves different tasks. Between the two spaces, information is cast towards each other via a novel cross- and dual-attention mechanism, mimicking the bottom-up and top-down processes of a human's cocktail party effect. It turns out that substantially discriminative and generalizable speaker representations can be learnt in severely interfered conditions via our self-supervised training. The experimental results verify this seeming paradox. The learnt speaker embedding has superior discriminative power than a standard speaker verification method; meanwhile, Tune-In achieves remarkably better speech separation performances in terms of SI-SNRi and SDRi consistently in all test modes, and especially at lower memory and computational consumption, than state-of-the-art benchmark systems.

本研究提出一种新颖的关注网络Tune-In，通过负面环境下的训练解决鸡尾酒会问题，其中学习了两个独立的空间，代表说话人知识和语音刺激，并通过交叉和双重注意机制协作地处理任务，并且在实验中表现出超越现有基准系统的口语分离性能。

模拟鸡尾酒会效应的注意力网络干扰训练下负面环境调整