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We address the problem of speech enhancement generalisation to unseenenvironments by performing two manipulations. First, we embed an additionalrecording from the environment alone, and use this embedding to alteractivations in the main enhancement subnetwork. Second, we scale the number ofnoise environments present at training time to 16,784 different environments.Experiment results show that both manipulations reduce word error rates of apretrained speech recognition system and improve enhancement quality accordingto a number of performance measures. Specifically, our best model reduces theword error rate from 34.04% on noisy speech to 15.46% on the enhanced speech.Enhanced audio samples can be found inhttps://speechenhancement.page.link/samples.

Scaling Speech Enhancement in Unseen Environments with Noise Embeddings