Universal Discrete-Domain Speech Enhancement
Abstract
In real-world scenarios, speech signals are inevitably corrupted by various types of interference, making speech enhancement (SE) a critical task for robust speech processing. However, most existing SE methods only handle a limited range of distortions, such as additive noise, reverberation, or band limitation, while the study of SE under multiple simultaneous distortions remains limited. This gap affects the generalization and practical usability of SE methods in real-world environments.To address this gap, this paper proposes a novel Universal Discrete-domain SE model called UDSE.Unlike regression-based SE models that directly predict clean speech waveform or continuous features, UDSE redefines SE as a discrete-domain classification task, instead predicting the clean discrete tokens quantized by the residual vector quantizer (RVQ) of a pre-trained neural speech codec.Specifically, UDSE first extracts global features from the degraded speech. Guided by these global features, the clean token prediction for each VQ follows the rules of RVQ, where the prediction of each VQ relies on the results of the preceding ones. Finally, the predicted clean tokens from all VQs are decoded to reconstruct the clean speech waveform. During training, the UDSE model employs a teacher-forcing strategy, and is optimized with cross-entropy loss. Experimental results confirm that the proposed UDSE model can effectively enhance speech degraded by various conventional and unconventional distortions, e.g., additive noise, reverberation, band limitation, clipping, phase distortion, and compression distortion, as well as their combinations. These results demonstrate the superior universality and practicality of UDSE compared to advanced regression-based SE methods.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach to speech enhancement that effectively addresses the limitations of existing models by leveraging discrete-domain classification techniques. This advancement holds substantial promise for enhancing the robustness and adaptability of speech processing technologies in real-world applications.
Methodology
The proposed UDSE model innovatively redefines speech enhancement as a discrete-domain classification task, utilizing a pre-trained neural speech codec and a residual vector quantizer (RVQ). This approach contrasts with traditional regression-based models, allowing for enhanced adaptability to various distortions. The methodology is well-structured, with clear delineation of the global feature extraction, token prediction, and speech decoding processes. The use of teacher-forcing during training is a notable strength, as it mitigates error propagation and enhances learning efficiency.
Results & evaluation
The experiments are comprehensive, evaluating UDSE against multiple baseline models across various conventional and unconventional distortion types. The authors provide both objective and subjective metrics, demonstrating UDSE's superior performance in restoring speech quality. The dataset construction is robust, incorporating diverse speech distortions, which strengthens the validity of the results. However, the paper could benefit from a more detailed discussion of the statistical significance of the results across all tasks.
Reproducibility
The implementation details are sufficiently described, including the architecture, training strategy, and dataset construction. The authors provide a demo URL and mention the use of a specific neural speech codec, which aids in reproducibility. However, the paper would benefit from sharing code or detailed instructions for replicating the experiments.
Limitations
While the UDSE model shows promise, it may still struggle with certain types of distortions that were not extensively covered in the experiments. Additionally, the reliance on a pre-trained neural speech codec may limit the model's applicability in scenarios where such codecs are not available or feasible. The subjective evaluation could also be influenced by listener biases, which may not be fully accounted for.
Broader impact
The UDSE model has significant implications for real-world applications in speech processing, including telecommunications, hearing aids, and automatic speech recognition systems. Its ability to handle a wide range of distortions enhances its practicality in diverse environments, potentially leading to improved communication clarity in challenging acoustic settings. The paper presents a novel approach to speech enhancement that effectively addresses the limitations of existing models by leveraging discrete-domain classification techniques. This advancement holds substantial promise for enhancing the robustness and adaptability of speech processing technologies in real-world applications.
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