Perceptual effects of reducing algorithmic latency on deep-learning based noise reductiona)

Low latency is an essential requirement for noise reduction in real-world devices such as hearing aids and cochlear implants. Reducing the algorithmic latency of a deep neural network charged with noise reduction allows additional time for other processing. However, a larger analysis window may be advantageous to the performance of the network. This trade-off is currently examined with regard to human speech-intelligibility performance. The algorithmic latency of the attentive recurrent network (ARN) was modified by reducing the size of the analysis time frame. The ARN model was talker, noise, and recording-channel independent, and fully causal. Listeners with hearing loss and with normal hearing heard sentences in babble at various signal-to-noise ratios. Large increases in intelligibility were observed as a result of noise reduction, especially for the listeners with hearing loss and at less favorable signal-to-noise ratios. Slightly larger objective measures of network performance were observed at larger latencies. But more critically, human performance was essentially unchanged as algorithmic latency was reduced from 20 to 10 or 5 ms. These results are discussed in the context of overall design and implementation of deep-learning based noise reduction, and information on latency requirements for human listeners is summarized.