Design of a Speech Enhancement-Based Deep Semantic Autoencoder for Wireless Communications

Xiaoling Han; Bin Lin; Nan Wu; Tony Q. S. Quek

cs.SDNov 5, 2026

Design of a Speech Enhancement-Based Deep Semantic Autoencoder for Wireless Communications

Authors: Xiaoling Han, Bin Lin, Nan Wu, Tony Q. S. Quek

Organizations: Department of Information Science and Technology of Dalian Maritime University, Dalian, China, 116026 · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore, 487372

Abstract

Speech communication plays a crucial role in the Internet of Things and autonomous networks. However, traditional communication systems suffer from insufficient semantic extraction and limited noise suppression capability. To address these issues, we propose a Speech enhancement-based Deep Semantic Autoencoder (S-DSA) communication system for wireless communications. It consists of a dual-branch semantic encoder, a channel encoder, a channel decoder, and two semantic decoders, enabling joint source-channel coding for robust transmission. A key novelty lies in an adaptive semantic noise decoupling mechanism that disentangles target speech semantics from noise representations through an interaction module, thus maintaining semantic consistency under low SNR conditions. Furthermore, a joint training strategy optimizes speech reconstruction and noise estimation, while a lightweight testing strategy reduces complexity by disabling the noise-decoding path. The proposed S-DSA enables more effective separation between speech and noise, and yields higher robustness as well as better reconstruction quality. These findings highlight the potential of the proposed S-DSA as a practical solution for next-generation intelligent wireless communications.

Figures & tables

Panel of Figure 1 — Fig. 1: Architecture of the proposed S-DSA.

End	Layer Category	Layer Name	Output Dimension
Transmitter	Dual-branch semantic encoder Only the semantic encoder S is shown; the semantic encoder N is identical.	Conv block (Semantic encoder S)	[ $B$ , $D_{\rm s}$ , $T$ , $F$ ]
		Conv block (Semantic encoder S)	[ $B$ , $2D_{\rm s}$ , $T$ , $F/2$ ]
		Conv block (Semantic encoder S)	[ $B$ , $4D_{\rm s}$ , $T$ , $F/4$ ]
		Conv stack (Semantic encoder S)	[ $B$ , $4D_{\rm s}$ , $T$ , $F/4$ ]
		Conv stack (Semantic encoder S)	[ $B$ , $T$ , $F/4$ , $4D_{\rm s}$ ]
		SSA (Semantic encoder S)	[ $B$ , $T$ , $F/4$ , $4D_{\rm s}$ ]

TABLE I: Architectural parameters of the proposed S-DSA.

Parameter	Setting
Implementation framework	PyTorch
Optimizer	Adam
Max training epochs	120
Learning rate	Initial $10^{-3}$ , minimum $10^{-4}$
Batch size	32
Validation split	20%

TABLE II: Simulation parameter settings of the proposed S-DSA.

Fig. 2: Waveform and spectrogram comparisons for speech enhancement.

Panel of Figure 5 — Fig. 3: Performance metrics versus channel SNR across different channels.

Fig. 4: Performance metrics versus environmental SNR on the AWGN channel.

Fig. 5: Training and validation loss on the AWGN channel.

Fig. 6: Performance metrics versus channel SNR on the AWGN channel under varying learning rates.

Tony Q. S. Quek (S’98-M’08-SM’12-F’18) received the B.E. and M.E. degrees in electrical and electronics engineering from the Tokyo Institute of Technology in 1998 and 2000, respectively, and the Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology in 2008. Currently, he is the Associate Provost (AI & Digital Innovation) and Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD). He also serves as the Director of the Future Communications R&D Programme, and the ST Engineering Distinguished Professor. He is a co-founder of Silence Laboratories and NeuroRAN. His current research topics include wireless communications and networking, network intelligence, non-terrestrial networks, open radio access network, AI-RAN, and 6G. Dr. Quek was honored with the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2015 SUTD Outstanding Education Awards – Excellence in Research, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, the 2020 Nokia Visiting Professor, the 2022 IEEE Signal Processing Society Best Paper Award, the 2024 IIT Bombay International Award For Excellence in Research in Engineering and Technology, the IEEE Communications Society WTC Recognition Award 2024, and the Public Administration Medal (Bronze). He is an IEEE Fellow, a WWRF Fellow, an AIIA Fellow, and a Fellow of the Academy of Engineering Singapore.

Table 12

Semantic Communication

Semantic communication sends meaning- or task-relevant information so receivers can act without reconstructing every transmitted bit or signal.

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Semantic Error Correction and Decoding for Short Block Codes

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This paper presents a semantic-enhanced receiver framework for transmitting natural language sentences over noisy wireless channels using multiple short block codes. After ASCII encoding, the sentence is divided into segments, each independently encoded with a short block code and transmitted over an AWGN channel. At the receiver, segments are decoded in parallel, followed by a semantic error correction (SEC) model, which reconstructs corrupted segments using language model context. We further propose the semantic list decoding (SLD), which generates multiple candidate reconstructions and selects the best one via weighted Hamming distance, and a semantic confidence-guided HARQ (SHARQ) mechanism that replaces CRC-based error detection with a confidence score, enabling selective segment retransmission without CRC overhead. All modules are designed and trained using bidirectional and auto-regressive transformers (BART). Simulation results demonstrate that the proposed scheme significantly outperforms conventional capacity-approaching short codes and long codes at the same rate. Specifically, SEC provides approximately 0.4 dB BLER gain over plain short-code transmission, while SLD extends this to 0.8 dB. Compared to transmitting the entire sentence as a single long 5G LDPC codeword, our approach significantly improves semantic fidelity and reduces decoding latency by up to 90%. SHARQ further provides an additional 1.5 dB gain over conventional HARQ.

Abstract

Semantic Communication

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