Speaker identification using Ultra‐Wideband measurement of voice

Abstract

Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current strategies such as acoustic models based on deep learning, voice bio‐metrics, and spectrogram analysis, have been identified with several drawbacks including vulnerability to altered voices, susceptibility to ambient noise, and the necessity for significant computational power. In response to these issues, the authors introduce a ground‐breaking method of voice identification using Ultra‐Wideband (UWB) technology. This method capitalises on the micro‐Doppler shifts associated with movements of the laryngeal prominence. The distinctive nature of these bio‐metric traits related to speech production provides superior resistance against common pitfalls of voice identification. The proposed model leverages the high‐resolution characteristics of UWB to register tiny variations in laryngeal movements produced during speech, thus forming a distinct voice profile for each speaker. Through rigorous testing, the proposed system demonstrated significant progress in voice identification, achieving close to 90% accuracy in controlled experimental settings. This breakthrough indicates that UWB‐enabled voice identification could have a profound effect on medical applications, providing potential improvements in diagnosing, monitoring, possibly treating speech disorders, and thereby shaping a future of enhanced and secured healthcare services.

Guest Editorial: Advances in AI‐assisted radar sensing applications

“…Zhenghui Li et al introduce an innovative approach to radarbased human activity recognition across six domains, with adaptive thresholding and holistic optimisation, significantly improving classification accuracy [2]. Li et al propose a groundbreaking voice identification method using Ultra-Wideband technology, leveraging micro-Doppler shifts during speech production to achieve close to 90% accuracy in healthcare applications [3]. [9].…”

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

“…Obtaining large-scale, accurately annotated datasets of aphasic speech is challenging due to the time-consuming nature of manual annotation and the limited availability of such data [15,16,19,24,25]. Without sufficient data for training, deep learning models may struggle to generalize effectively across different types and severities of aphasia, leading to reduced performance and reliability in real-world applications [22,26].…”

“…Balancing model complexity with computational efficiency is essential for developing effective deep learning solutions for aphasia [25,26]. While complex models may offer superior performance in recognizing the nuanced features of aphasic speech, they often come with increased computational costs and resource requirements, making them impractical for real-time applications or deployment on resource-constrained devices.…”

“…Exploring techniques such as model pruning, quantization, or knowledge distillation can help reduce the computational complexity of deep learning models while preserving their predictive performance, making them more suitable for resource-constrained environments or real-time applications. Balancing model complexity and computational efficiency is crucial for deploying deep learning models in real-world applications, such as aphasia assessment [25]. By optimizing model architectures and training procedures to strike the right balance between complexity and efficiency, researchers can develop models that are both accurate and computationally efficient, making them suitable for real-time deployment in clinical settings.…”