  Modern smartphones possess hardware for audio acquisition and to perform
speech processing tasks such as speaker recognition and health assessment.
However, energy consumption remains a concern, especially for
resource-intensive DNNs. Prior work has improved the DNN energy efficiency by
utilizing a compact model or reducing the dimensions of speech features. Both
approaches reduced energy consumption during DNN inference but not during
speech acquisition. This paper proposes using a masking kernel integrated into
gradient descent during DNN training to learn the most energy-efficient speech
length and sampling rate for windowing, a common step for sample construction.
To determine the most energy-optimal parameters, a masking function with
non-zero derivatives was combined with a low-pass filter. The proposed approach
minimizes the energy consumption of both data collection and inference by 57%,
and is competitive with speaker recognition and traumatic brain injury
detection baselines.
