Adversarial training for data-driven speech enhancement without parallel corpus

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Abstract
This paper describes a way of performing data-driven speech enhancement for noise robust automatic speech recognition (ASR), where we train a model for speech enhancement without a parallel corpus. Data-driven speech enhancement with deep models has recently been investigated and proven to be a promising approach for ASR. However, for model training, we need a parallel corpus consisting of noisy speech signals and corresponding clean speech signals for supervision. Therefore a deep model can be trained only with a simulated dataset, and we cannot take advantage of a large number of noisy recordings that do not have corresponding clean speech signals. As a first step towards model training without supervision, this paper proposes a novel approach introducing adversarial training for a time-frequency mask estimator. Our cost function for model training is defined by discriminators instead of by using the distance between the model outputs and the supervision. The discriminators distinguish between true signals and enhanced signals obtained with time-frequency masks estimated with a mask estimator. The mask estimator is trained to cheat the discriminators, which enables the mask estimator to estimate the appropriate time-frequency masks without a parallel corpus. The enhanced signal is finally obtained with masking-based beamforming. Experimental results show that, even without exploiting parallel data, our speech enhancement approach achieves improved ASR performance compared with results obtained with unprocessed signals and achieves comparable ASR performance to that obtained with a model trained with a parallel corpus based on a minimum mean squared error (MMSE) criterion.

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