arXiv:2606.18611v1 Announce Type: cross Abstract: We propose a parameter-efficient speech enhancement framework, Quaternion Conformer GAN (QC-GAN), which combines a Quaternion Conformer generator with MetricGAN-based training. The Hamilton product encodes the magnitude and phase via structured weight sharing, reducing the number of layer parameters while preserving their interdependencies. A metric-learning discriminator was employed to maximize perceptual quality by optimizing the approximate perceptual evaluation scores. On the VoiceBank+DEMAND dataset, QC-GAN achieved a Perceptual Evaluatio
The continuous advancements in AI research, particularly in generative models and efficient architectures, are driving innovations in speech processing.
This development indicates progress towards more efficient and high-fidelity AI models, reducing computational demands for complex tasks like speech enhancement, which has broad applications.
The proposed QC-GAN demonstrates a method to achieve high-quality speech enhancement with fewer parameters, suggesting a shift towards more resource-friendly yet powerful AI models.
- · AI researchers
- · Speech technology companies
- · Edge AI device manufacturers
- · Inefficient AI model developers
Improved speech enhancement capabilities in various applications including telecommunications, virtual assistants, and accessibility tools.
Reduced computational costs and energy consumption for AI-driven audio processing, enabling wider deployment on resource-constrained devices.
Accelerated development of real-time, high-quality audio interactions in augmented and virtual reality environments.
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Read at arXiv cs.AI