TripleC Learning and Lightweight Speech Enhancement for Multi-Condition Target Speech Extraction
Abstract
In our recent work, we proposed Lightweight Speech Enhancement Guided Target Speech Extraction (LGTSE) and demonstrated its effectiveness in multi-speaker-plus-noise scenarios. However, real-world applications often involve more diverse and complex conditions, such as one-speaker-plus-noise or two-speaker-without-noise. To address this challenge, we extend LGTSE with a Cross-Condition Consistency learning strategy, termed TripleC Learning. This strategy is first validated under multi-speaker-plus-noise condition and then evaluated for its generalization across diverse scenarios. Moreover, building upon the lightweight front-end denoiser in LGTSE, which can flexibly process both noisy and clean mixtures and shows strong generalization to unseen conditions, we integrate TripleC learning with a proposed parallel universal training scheme that organizes batches containing multiple scenarios for the same target speaker. By enforcing consistent extraction across different conditions, easier cases can assist harder ones, thereby fully exploiting diverse training data and fostering a robust universal model. Experimental results on the Libri2Mix three-condition tasks demonstrate that the proposed LGTSE with TripleC learning achieves superior performance over condition-specific models, highlighting its strong potential for universal deployment in real-world speech applications.
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 significant advancement in target speech extraction by introducing TripleC Learning, which enhances the robustness of speech enhancement models across diverse acoustic conditions. The methodology is innovative, and the results demonstrate its potential for practical applications, although further validation and reproducibility efforts are needed.
Methodology
The paper introduces a novel approach termed TripleC Learning, which enhances the existing Lightweight Speech Enhancement Guided Target Speech Extraction (LGTSE) model by enforcing cross-condition consistency during training. This methodology is well-structured, leveraging the strengths of easier conditions to improve performance in more challenging scenarios. The integration of a parallel universal training scheme is particularly innovative, as it allows the model to learn from multiple conditions simultaneously, thereby improving generalization across diverse acoustic environments. The use of a lightweight denoiser (GTCRN) is also a strong point, as it maintains efficiency while achieving robust performance.
Results & evaluation
The experiments are conducted on the Libri2Mix dataset, which is a well-regarded benchmark in the field. The results demonstrate significant improvements over condition-specific models, validating the proposed method's effectiveness. The paper provides comprehensive comparisons with existing approaches, showcasing the superiority of the proposed model across various metrics (SI-SDR, PESQ, STOI). However, the evaluation could benefit from additional datasets or real-world scenarios to further substantiate claims of generalization.
Reproducibility
The paper lacks detailed implementation specifics, such as hyperparameter settings and model architecture diagrams, which are crucial for reproducibility. While it mentions the use of Adam optimizer and training strategies, more explicit details would enhance the ability of other researchers to replicate the study. Additionally, the absence of a public code repository limits accessibility for further exploration and validation of the findings.
Limitations
One notable limitation is the slight performance drop observed in the easier 1-speaker-plus-noise condition when applying TripleC Learning. This suggests a trade-off that may need further investigation. Additionally, the model's performance in real-world applications remains to be tested, as the experiments are confined to a controlled dataset. The reliance on a specific dataset (Libri2Mix) may also limit the generalizability of the findings to other speech enhancement tasks.
Broader impact
The proposed model has significant implications for real-world applications in automatic speech recognition, hearing aids, and communication systems, especially in environments with varying noise conditions. Its lightweight nature makes it suitable for deployment in resource-constrained devices, potentially improving accessibility to advanced speech processing technologies. The focus on universal models that generalize across conditions aligns well with current trends in machine learning, emphasizing the need for adaptable and efficient solutions. The paper presents a significant advancement in target speech extraction by introducing TripleC Learning, which enhances the robustness of speech enhancement models across diverse acoustic conditions. The methodology is innovative, and the results demonstrate its potential for practical applications, although further validation and reproducibility efforts are needed.
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