Multi-task self-supervised learning for Robust Speech Recognition

25 Jan 2020Mirco RavanelliJianyuan ZhongSantiago PascualPawel SwietojanskiJoao MonteiroJan TrmalYoshua Bengio

Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth)... (read more)

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