Deep convolutional neural network-based speech enhancement to improve speech intelligibility and quality for hearing-impaired listeners

AbstractIn this paper, we propose a deep learning-based speech enhancement (DLSE) method to improve speech intelligibility for the hearing-impaired listeners. The algorithm decomposes the noisy speech signal into frames (as features) and feeds them to the deep convolutional neural networks (DCNNs) to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated cochlear implant (CI) channels for electrical stimulation, as in traditionaln-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 12 CI users using two types of background noises such as fan and music sounds. The architectur...

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