###### Abstract
While the field is still evolving, existing studies predominantly focus on image- or text-based communication. In contrast to frameworks such as D\({}^{2}\)-JSCC, which focuses on image-based semantic communications through deep source-channel coding, our work introduces structured feature discretization, allowing it to be integrated with traditional digital telecommunication frameworks. Moreover, while HybridBSC incorporates semantic- and bit-based transmission, a crucial limitation of HybridBSC is the lack of an explicit bit allocation for hybrid bit-semantic transmission. Without a defined bit burden framework, the system's efficiency and adaptability to varying channel conditions remain vague. In addition, its main focus is on image transmission. Speech signals are required to preserve perceptual quality rather than solely minimize pixel distortion. [PERSON] & [PERSON] [21] present a transmission technique for transmitting digital signals by utilizing images as carrier signals. Although this study addresses digital signal transmission within a semantic communication framework, its methodology fundamentally differs from ours in both encoding strategy and transmission approach. These situations leave semantic-aware digital speech transmission underexplored. To address these gaps, this paper proposes a semantic-aware digital speech communication system that serves as an intermediate transition between conventional and fully end-to-end semantic communication models. Unlike purely deep learning-based systems, our model is capable of the following:  
* Extracts structured semantic representations of speech while ensuring compatibility with conventional digital transmission techniques. * Discreteizes feature tensors before transmission, initiating a pathway for integration with existing bit-based communication systems. * Mirrors the transmitter architecture at the receiver side, reconstructing speech with high perceptual quality while maintaining structured signal integrity. Our system is designed to function under practical wireless conditions, incorporating stochastic channel models (Additive White Gaussian Noise: AWGN, Rayleigh, Rician multipath fading) while ensuring robust speech transmission across varying signal-to-noise (SNR) levels. Performance is evaluated using the Signal-to-Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI) to comprehensively assess both signal fidelity and intelligibility. By integrating semantic-aware encoding within a structured digital transmission framework, our system remains applicable to existing telecommunication infrastructures while demonstrating the potential of deep learning-based feature representation and reconstruction. This hybrid approach enables gradual adoption in practical deployments, making it more accessible for real-world applications, such as low-bandwidth speech communication, robust voice transmission over wireless networks, and AI-assisted speech processing for edge computing devices. This study provides a scalable and interpretable approach to integrating semantic-aware speech processing into digital communication frameworks, bridging the gap between conventional wireless systems and emerging AI-driven transmission paradigms. Our core novelty lies in the pragmatic fusion of deep learning-driven semantic speech understanding with established digital communication principles. By moving beyond purely end-to-end neural models, we introduce a structured discretization process that allows semantic features to be transmitted as digital signals, initiating a pathway for integration with existing wireless infrastructure. Specifically, by focusing on speech, we address the critical need for perceptual quality preservation in voice communications--a requirement that differentiates it significantly from image- and text-centric semantic models. It serves as a transitional framework between conventional source-channel coding techniques and end-to-end deep learning-based semantic communication models.