Masking Kernel for Learning Energy-Efficient Representations for Speaker Recognition and Mobile Health

TL;DR

This paper introduces a masking kernel integrated into DNN training to optimize speech windowing parameters, significantly reducing energy consumption in smartphone-based speaker recognition and health assessment tasks.

Contribution

It proposes a novel masking kernel method that learns energy-efficient speech acquisition parameters during DNN training, addressing energy use in both data collection and inference.

Findings

Reduces overall energy consumption by 57%.

Achieves competitive performance in speaker recognition and health detection.

Optimizes speech windowing parameters for energy efficiency.

Abstract

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.