Abstract 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.

中文翻译：

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用于助听器语音活动检测的级联线性检测器和神经网络

摘要 随着年龄的增长，听力损失是一个常见问题，会导致抑郁、痴呆风险和认知能力下降等问题。助听器是计算受限的设备，可提供解决此问题的可能性，从而提高人们的生活质量。应该在这些设备中实现的典型算法是语音活动检测。在这项工作中，级联检测器用于在保持相同性能的同时降低计算成本或在保持相同计算成本的同时提高性能。这是通过两级检测器实现的。在第一阶段，线性系统确定检测是否可以轻松执行，或者需要具有更复杂的基于神经网络的检测的第二阶段。这条路，一些决定是在不使用复杂检测器的情况下做出的。结果表明，在使用相同资源量的情况下，系统误差可降低高达8.5%。此外，在可负担得起的助听器实施方案中，该错误是最低的。