Redefining Single-Channel Speech Development: The xLSTM-SENet Approach

Speech processing systems often struggle to deliver clear sound in noisy environments. This challenge affects applications such as hearing aids, automatic speech recognition (ASR), and speaker authentication. Common single-channel speech enhancement (SE) programs use neural network architectures such as LSTMs, CNNs, and GANs, but they are not without limitations. For example, attention-based models such as Conformers, although powerful, require extensive computational resources and large data sets, which may not be practical for some applications. These issues highlight the need for more scalable and efficient alternatives.

Introducing xLSTM-SENet

To address these challenges, researchers from Aalborg University and Oticon A/S developed xLSTM-SENet, the first SE system based on a single xLSTM. This system builds on the Extended Short Term Memory (xLSTM) architecture, which refines traditional LSTM models by introducing exponential gating and matrix memory. These enhancements solve some of the limitations of standard LSTMs, such as limited storage capacity and limited parallelism. By integrating xLSTM into the MP-SENet framework, the new system can effectively process both magnitude and phase spectra, providing a systematic approach to speech enhancement.

Technical Overview and Benefits

xLSTM-SENet is designed with an encoder-decoder-time-frequency (TF) domain structure. At its core are the TF-xLSTM blocks, which use mLSTM layers to capture both temporal and frequency dependence. Unlike traditional LSTMs, mLSTMs use an exponential gate for more precise control of storage and matrix-based memory design for capacity expansion. The two-dimensional structure also improves the model's ability to use context information from both past and future frames. In addition, the system includes special decoders for amplitude and phase spectra, which contribute to the improvement of speech quality and intelligibility. These innovations make xLSTM-SENet more efficient and suitable for devices with constrained computing resources.

Performance and results

Experiments using the VoiceBank+DEMAND dataset highlight the effectiveness of xLSTM-SENet. The program achieves results comparable to or better than state-of-the-art models such as SEMamba and MP-SENet. For example, it recorded a Perceptual Evaluation of Speech Quality (PESQ) score of 3.48 and a Short-Time Objective Intelligibility (STOI) score of 0.96. Additionally, composite metrics such as CSIG, CBAK, and COVL showed significant improvement. Ablation studies have emphasized the importance of features such as exponential gating and bidirectionality in improving performance. Although the program requires longer training periods than some maintenance-based models, its overall performance demonstrates its value.

The conclusion

xLSTM-SENet provides a logical answer to the challenges of single-channel speech optimization. By leveraging the power of xLSTM architectures, the system balances scalability and robustness. This work not only advances the state of speech enhancement technology but also opens the door to applications in real-world situations, such as hearing aids and speech recognition systems. As these techniques continue to evolve, they promise to make high-quality speech processing more accessible and workable for a variety of needs.

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Nikhil is an intern consultant at Marktechpost. He is pursuing a dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is constantly researching applications in fields such as biomaterials and biomedical sciences. With a strong background in Material Science, he explores new developments and creates opportunities to contribute.