Linear Detector And Neural Networks In Cascade For Voice Activity Detection In Hearing Aids

Hearing loss is a common issue when people become older, resulting in problems such as depression, risk of dementia, and cognitive decline, among others. Hearing aids are computationally constrained devices that offer the possibility of solving this issue, thus improving people's quality of life. A typical algorithm that should be implemented in these devices is Voice Activity Detection. In this work, cascade detectors are applied to reduce the computational cost while maintaining the same performance or to increase the performance while maintaining the same computational cost. This is achieved by a two-stage detector. In the first stage, a linear system determines whether the detection can be easily carried out, or a second stage with a more complex neural-network-based detection is required. This way, some of the decisions are taken without using the complex detector. The results show that the system error can be reduced up to 8.5% while using the same amount of resources. Moreover, the error is the lowest among the proposals that are affordably implemented in hearing aids. (C) 2020 The Author(s). Published by Elsevier Ltd.