# Design of a Dual-Path Speech Enhancement Model
## 1 Introduction  
Speech enhancement (SE) refers to the process of improving the quality and intelligibility of speech signals by reducing background noise and distortions. It plays a crucial role in various applications [1, 2, 3, 4, 5], including automatic speech recognition (ASR), speech coding, and communication systems, which often demand low-latency or real-time processing, and therefore, require causal models that operate without future information. An effective SE model must achieve two fundamental objectives: removing unwanted background noise and restoring speech signals. By successfully addressing both aspects, an SE model can enhance speech clarity, intelligibility, and naturalness. Traditional SE techniques, including statistics-based methods, spectral subtraction, and Wiener filtering, have been widely used [6, 7]. However, their effectiveness is limited in real-world scenarios with highly non-stationary noises, such as speech babble and wind noise. These types of noise exhibit complex spectral variations over time, while many methods rely on strong stationarity assumptions--such as fixed noise statistics or time-invariant spectral properties. With the advent of deep learning, Deep neural network (DNN)-based SE models have demonstrated superior performance, leveraging large-scale datasets and powerful feature extraction capabilities [8, 9, 10, 11]. In particular, frequency-domain SE models have gained attention for their effectiveness even in real-world scenarios [12, 13]. The multi-stage SE system, one of the most successful approaches among frequency-domain SE models, has demonstrated remarkable performance by sequentially refining different aspects of the speech signal [14; 15; 16]. This approach allows for more precise enhancement of frequency components, leading to improved speech intelligibility and quality. For example, [PERSON] et al. [14] adopts a two-stage approach, where the first stage estimates the magnitude spectrum, and the second stage refines both the magnitude and phase spectra. However, this sequential processing may introduce latency and limit feature interaction between stages, as the next stage can only refine the output of the previous stage rather than jointly optimizing both. Building on this, researchers have further extended the multi-stage approach into a multi-branch structure, where multiple branches operate in parallel rather than sequentially [17; 18; 19]. This parallel processing allows different aspects of the speech signal to be processed in different branches. For example, [PERSON] et al. [17] utilize two decoders with one shared encoder for magnitude recovery and complex spectral detail compensation, thus enhancing SE performance without additional latency. On the other hand, [PERSON] et al. [18] adopts two parallel branches (using two encoder-decoder pairs) operating in the complex and magnitude domains, respectively, equipped with attention modules. However, recent studies have shown that some SE models, instead of improving performance, actually degrade ASR results when used as a preprocessing step, and the speech intelligibility metric scores tend not to improve [20]. This unexpected finding suggests that during the SE process, the model may overly focus on noise suppression, leading to the distortion of linguistic information essential for speech recognition. From this perspective, it is essential for SE models to achieve consistently high performance across various evaluation metrics. In this paper, we propose a dual-path SE model, which extends the multi-branch architecture by introducing an auxiliary refinement path. This design enables the model to suppress noise and maintain speech components more effectively without sacrificing critical speech features, such as spectral tilt and formant information, or violating causal constraints. As a result, experimental results show that our model achieves better performance across various noise reduction and speech restoration metrics, rather than single-path SE model.