2024/07/12 by Yeona Hong, Hong, Yeona, Woo-Jin Chung +3 · 1 citation
Computer Science · #Algorithm #Artificial intelligence #Computer science #Quantization (signal processing) #Speaker recognition #Speaker verification #Speech Recognition and Synthesis #Speech recognition
paper · pdf · doi:10.48550/arxiv.2407.08991
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/07/12 · openalex created_date 2024/07/16 · openalex updated_date 2026/07/28
As Deep Neural Networks (DNNs) rapidly advance in various fields, including speech verification, they typically involve high computational costs and substantial memory consumption, which can be challenging to manage on mobile systems. Quantization of deep models offers a means to reduce both computational and memory expenses. Our research proposes an optimization framework for the quantization of the speaker verification model. By analyzing performance changes and model size reductions in each layer of a pre-trained speaker verification model, we have effectively minimized performance degradation while significantly reducing the model size. Our quantization algorithm is the first attempt to maintain the performance of the state-of-the-art pre-trained speaker verification model, ECAPATDNN, while significantly compressing its model size. Overall, our quantization approach resulted in reducing the model size by half, with an increase in EER limited to 0.07%.