Convex Low-resource Accent-Robust Language Detection in Speech Recognition
Abstract
Globalization and multiculturalism continue to produce increasingly diverse speech varieties. Yet current spoken dialogue systems frequently fail on under-represented dialects and accents, often misidentifying the input language and causing cascading failures in downstream dialogue tasks. Addressing this dialectal variance under low-resource constraints remains an open challenge, as standard fine-tuning is computationally expensive and prone to overfitting on high-dimensional speech data. We propose Convex Language Detection (CLD), a novel framework that integrates theoretically grounded convex optimization techniques into the spoken dialogue systems pipeline. Our method is efficiently implemented via multi-GPU Alternating Direction Method of Multipliers (ADMM) in JAX, thus providing global optimality guarantees and fast training in polynomial time. Theoretically, we prove that our convex objective induces certified margin stability and provide guarantees against feature perturbations. Empirically, we demonstrate sample efficiency and robustness to input dialectical variation, achieving 97-98% accuracy in challenging low-resource regimes. Our open-source package is available at https://pypi.org/project/jaxcld/
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 main contribution of this paper is the introduction of the Convex Language Detection framework, which leverages convex optimization techniques to achieve robust language identification in low-resource speech recognition settings. This work represents a significant advancement in addressing the challenges posed by dialectal variations, providing both theoretical insights and empirical validation of its effectiveness.
Methodology
The paper introduces the Convex Language Detection (CLD) framework, which employs convex optimization techniques to improve language detection in speech recognition systems under low-resource conditions. The methodology is well-grounded in theoretical principles, providing global optimality guarantees and robustness against feature perturbations. The use of the Alternating Direction Method of Multipliers (ADMM) for efficient implementation is a notable strength, allowing for scalable training on high-dimensional audio data. The integration of convex reformulations into the ASR pipeline is innovative, particularly in the context of dialectal variations.
Results & evaluation
The experiments are comprehensive, covering multiple languages and dialects, with a focus on low-resource settings. The authors demonstrate the effectiveness of CLD through various metrics, including detection accuracy and Word Error Rate (WER), achieving impressive results compared to baseline models. The use of diverse datasets enhances the credibility of the results, and the empirical validation supports the theoretical claims made in the paper.
Reproducibility
The authors provide an open-source implementation of their framework, which is crucial for reproducibility. The availability of the JAX package allows other researchers to replicate the experiments and build upon the work. However, more detailed documentation of the experimental setup and hyperparameter choices would further enhance reproducibility.
Limitations
One limitation is the reliance on specific datasets, which may not capture the full diversity of dialectal variations in real-world applications. Additionally, while the convex optimization approach offers theoretical guarantees, its practical performance in extremely noisy or diverse environments remains to be fully validated. The paper could also benefit from a deeper exploration of the trade-offs between computational efficiency and model complexity.
Broader impact
The CLD framework has significant implications for improving accessibility and inclusivity in spoken dialogue systems, particularly for underrepresented dialects and accents. By enhancing the robustness of ASR systems, this work contributes to democratizing access to technology for diverse linguistic communities, which is increasingly important in our globalized society. The main contribution of this paper is the introduction of the Convex Language Detection framework, which leverages convex optimization techniques to achieve robust language identification in low-resource speech recognition settings. This work represents a significant advancement in addressing the challenges posed by dialectal variations, providing both theoretical insights and empirical validation of its effectiveness.
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