###### Abstract  
Recent advances in deep learning have fostered a transition from the traditional, bit-centric paradigm of [PERSON]'s information theory to a semantic-oriented approach, emphasizing the transmission of meaningful information rather than mere data fidelity. However, black-box AI-based semantic communication lacks structured discretization and remains dependent on analog modulation, which presents deployment challenges. This paper presents a new semantic-aware digital speech communication system, named Hybrid-DeepSCS, a stepping stone between traditional and fully end-to-end semantic communication. Our system comprises the following parts: a semantic encoder for extracting and compressing structured features, a standard transmitter for digital modulation including source and channel encoding, a standard receiver for recovering the bitstream, and a semantic decoder for expanding the features and reconstructing speech. By adding semantic encoding to a standard digital transmission, our system works with existing communication networks while exploring the potential of deep learning for feature representation and reconstruction. This hybrid method allows for gradual implementation, making it more practical for real-world uses like low-bandwidth speech, robust voice transmission over wireless networks, and AI-assisted speech on edge devices. The system's compatibility with conventional digital infrastructure positions it as a viable solution for IoT deployments, where seamless integration with legacy systems and energy-efficient processing are critical. Furthermore, our approach addresses IoT-specific challenges such as bandwidth constraints in industrial sensor networks and latency-sensitive voice interactions in smart environments. We test the system under various channel conditions using Signal-to-Distortion Ratio (SDR), PESQ, and STOI metrics. The results show that our system delivers robust and clear speech, connecting traditional wireless systems with the future of AI-driven communication. The framework's adaptability to edge computing architectures further underscores its relevance for IoT platforms, enabling efficient semantic processing in resource-constrained environments. s +
Footnote †: journal: Electronics  
heterogeneous devices--from industrial sensors to voice-enabled edge nodes--require adaptive communication frameworks that balance efficiency, intelligibility, and interoperability. Unlike conventional approaches, which primarily aim to recover transmitted symbols, semantic communication systems prioritize the preservation of meaning and intelligibility. To this end, modern communication networks evolve toward 6G and beyond, and new paradigms that prioritize semantic meaning over exact bit recovery have emerged. This shift is particularly important for bandwidth-limited and noisy environments, where the reconstruction of the underlying semantic content can prove to be more beneficial than the sole recovery of the symbols. Such environments are ubiquitous in IoT applications, ranging from smart factories with high electromagnetic interference to urban deployments with dense wireless traffic. The transition from technical-level communication (Level A) to semantic-level communication (Level B) was first theorized by [PERSON] [2], who proposed that communication systems should not only transmit symbols accurately but also preserve their intended meaning. Several studies have attempted to formalize semantic information theory, laying the groundwork for modern AI-driven communication paradigms. [PERSON] et al. [3] extend [PERSON]'s classic information theory (CIT) [1] to incorporate semantic-level communication that goes beyond bit-accurate transmission to meaning-aware transmission. They introduce a model-theoretic approach to semantic data compression and reliable transmission. [PERSON] & [PERSON] [4] formalize semantic information processing by defining entropy, compression, and transmission limits. [PERSON] et al.