ParaGSE: Parallel Generative Speech Enhancement with Group-Vector-Quantization-based Neural Speech Codec
Abstract
Recently, generative speech enhancement has garnered considerable interest; however, existing approaches are hindered by excessive complexity, limited efficiency, and suboptimal speech quality. To overcome these challenges, this paper proposes a novel parallel generative speech enhancement (ParaGSE) framework that leverages a group vector quantization (GVQ)-based neural speech codec. The GVQ-based codec adopts separate VQs to produce mutually independent tokens, enabling efficient parallel token prediction in ParaGSE. Specifically, ParaGSE leverages the GVQ-based codec to encode degraded speech into distinct tokens, predicts the corresponding clean tokens through parallel branches conditioned on degraded spectral features, and ultimately reconstructs clean speech via the codec decoder. Experimental results demonstrate that ParaGSE consistently produces superior enhanced speech compared to both discriminative and generative baselines, under a wide range of distortions including noise, reverberation, band-limiting, and their mixtures. Furthermore, empowered by parallel computation in token prediction, ParaGSE attains about a 1.5-fold improvement in generation efficiency on CPU compared with serial generative speech enhancement approaches.
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 ParaGSE, a novel framework for parallel generative speech enhancement that leverages a GVQ-based neural speech codec to achieve significant improvements in speech quality and processing efficiency. The technical contributions are substantial, addressing key challenges in the field and demonstrating the potential for practical applications in real-world scenarios.
Methodology
The proposed methodology, ParaGSE, introduces a novel framework for generative speech enhancement that utilizes a group vector quantization (GVQ)-based neural speech codec. This approach is innovative in its use of separate VQs for independent token generation, which facilitates efficient parallel computation. The architecture is well-structured, employing a combination of convolutional layers, BiLSTM, and Conformer blocks to extract features and predict clean tokens. The methodology is sound, with a clear explanation of the components and their interactions, although it could benefit from more detailed comparisons with existing methods in terms of computational complexity.
Results & evaluation
The experimental evaluation is robust, featuring a comprehensive set of experiments that assess the performance of ParaGSE against various baseline models across multiple distortion types. The paper includes both objective and subjective metrics, providing a well-rounded view of the model's effectiveness. The dataset construction is thorough, utilizing real-world noise and reverberation conditions, which enhances the relevance of the findings. However, the paper could improve by including more detailed statistical analyses of the results and discussing the significance of the findings more explicitly.
Reproducibility
The paper provides sufficient implementation details, including the architecture, training criteria, and experimental setup, which aids in reproducibility. The availability of codes and speech samples on the provided URL is a positive aspect, although the lack of a direct GitHub repository may limit accessibility for some researchers.
Limitations
One limitation is the potential complexity of the model, which may hinder deployment in real-time applications. Additionally, while the paper claims efficiency improvements, it does not provide a detailed comparison of the computational costs associated with the proposed method versus the baselines, which could be crucial for practical applications. There is also a noted performance gap in intrusive metrics like LSD compared to discriminative models, which could be a concern for certain applications.
Broader impact
The proposed ParaGSE framework has significant potential for real-world applications in speech enhancement, particularly in environments with various distortions. Its efficiency and ability to produce high-quality speech restoration could benefit communication technologies, assistive listening devices, and speech recognition systems. The advancements in generative models for speech enhancement also contribute to the broader field of audio processing and machine learning. The paper presents ParaGSE, a novel framework for parallel generative speech enhancement that leverages a GVQ-based neural speech codec to achieve significant improvements in speech quality and processing efficiency. The technical contributions are substantial, addressing key challenges in the field and demonstrating the potential for practical applications in real-world scenarios.
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