A Low-Power Speech-Based Depression Recognition Processor With Hierarchical Local–Global Network

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Abstract
Depression is a critical public health concern characterized by underdiagnosis, often due to stigma, lack of awareness, and reluctance to seek help. Cases of delayed intervention could be alleviated by wearable solutions which enable continuous and unobtrusive monitoring of depression indicators. Compared to electroencephalogram (EEG)-based and video-based depression recognition, speech-based approaches can be performed without deliberate user attention. However, due to the limited accuracy of existing algorithms and constrained resources at edge, performing accurate speech-based depression recognition on wearable platforms remains a challenge. Therefore, to achieve unobtrusive, accurate, and efficient depression recognition at edge, this work presents a hierarchical local–global network (HLG-Net) and optimized processor design for speech-based depression recognition. The proposed HLG-Net integrates convolutional neural networks (CNNs) with multihead attention (MHA) mechanism to simultaneously capture local acoustic features and global utterance-level coherence, enhancing depression stage recognition. For efficient processor design, a cross-layer buffered dataflow is proposed for efficient data handling, reducing data storage by 98.77%. The computing unit (CU) employs layer fusion, operator optimization, and quantization techniques to further improve resource utilization and reduce power consumption while preserving recognition accuracy. System-level low-power techniques such as clock/input gating and near-threshold design for application specific integrated circuit (ASIC) further reduce power consumption. The proposed processor implemented on field-programmable gate array (FPGA) (XC7Z100-2FFG900) achieves the lowest reported mean absolute error (MAE) of 5.13 on AVEC 2014 database. The 180-nm ASIC implementation shows a simulated power consumption of $17.4~\mu $ W at 0.4 V. The results demonstrate the feasibility of accurate and efficient speech-based depression recognition on wearables.
Funding Information
  • National Natural Science Foundation of China (62304099)
  • National Natural Science Foundation of China (62274081)
  • Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20230807092359001)
  • Guangdong (2023QN10X177)

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