The Speech Enhancement via Attention Masking Network: An End-to-end System for Joint Suppression of Noise and Reverberation

A neural network-based end-to-end single-channel speech enhancement system designed for joint suppression of noise and reverberation, which can include attention masking. The neural network architecture can contain both an enhancement and an autoencoder path, so that disabling the masking mechanism causes reconstruction of the input speech signal. The autoencoder path and the enhancement can be simultaneously trained using a loss function that includes a perceptually-motivated waveform distance measure. Examples enable dynamic control of the level of suppression applied via a minimum gain level. A novel loss function can be utilized to simultaneously train both the enhancement and the autoencoder paths, which includes a perceptually-motivated waveform distance measure. Examples provide significant levels of noise suppression while maintaining high speech quality. Examples can also improve the performance of automated speech systems, such as speaker and language recognition, when used as a pre-processing step.