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Current Research Journal of Philological Sciences

Peer Reviewed | Open Access | E-ISSN: 2767-3758
Published Article

Improving Physiological Audio Transmission Using Spectral Balancing and Data Integration

Improving Physiological Audio Transmission Using Spectral Balancing and Data Integration

Authors

  • Dr. Rafael Henrique Costa
    Department of Philological Studies Federal University of Rio de Janeiro Rio de Janeiro, Brazil
  • Dr. Mariana Alves Pereira
    School of Language and Literature University of São Paulo São Paulo, Brazil

Keywords:

Physiological audio transmission bone-conducted speech spectral balancing

Abstract

Physiological audio transmission, particularly body-conducted and non-acoustic speech signals, has emerged as a critical research domain in robust communication systems operating under adverse acoustic environments. Conventional air-conducted speech processing techniques suffer from severe degradation in high-noise conditions, motivating the exploration of alternative sensing modalities such as bone-conducted microphones, non-audible murmur (NAM) sensors, and multi-sensor fusion frameworks. However, these modalities inherently produce distorted spectral characteristics, limited bandwidth signals, and reduced intelligibility, thereby necessitating advanced enhancement strategies.

This study proposes a comprehensive analytical and technical framework for improving physiological audio transmission through the integration of spectral balancing techniques and multi-source data fusion. The research systematically examines how spectral equalization can compensate for frequency attenuation and distortion inherent in body-conducted signals while leveraging data integration strategies to reconstruct high-fidelity speech representations. Building upon prior work in multi-sensor signal processing, adaptive filtering, and statistical voice conversion, the study introduces a unified architecture that combines spectral correction with cross-modal feature alignment.

The methodological foundation of this research integrates classical signal processing approaches, such as linear predictive modeling and comb filtering, with modern machine learning-based reconstruction techniques. The proposed framework evaluates the interplay between signal enhancement and sensor-level integration, emphasizing robustness, scalability, and real-time applicability. Empirical analysis demonstrates that combining spectral balancing with sensor fusion significantly enhances intelligibility, reduces noise artifacts, and improves recognition accuracy in extreme environments.

Furthermore, the study critically analyzes limitations associated with sensor noise, alignment errors, and computational complexity. It highlights the trade-offs between signal fidelity and processing overhead while proposing optimization strategies for practical deployment. The findings contribute to the advancement of physiological speech processing by offering a structured approach to overcoming inherent limitations in non-acoustic signal modalities.

This research holds implications for applications in defense communication systems, assistive technologies, medical diagnostics, and human-computer interaction, where reliable speech transmission is essential under challenging environmental conditions.

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