论文标题
使用重量量化的基于DNN的扬声器验证的模型压缩
Model Compression for DNN-based Speaker Verification Using Weight Quantization
论文作者
论文摘要
基于DNN的说话者验证（SV）模型在相对较高的计算成本下表现出明显的性能。可以应用模型压缩以减少较低资源消耗的模型大小。本研究利用了重量量化，以压缩两个广泛使用的SV模型，即ECAPA-TDNN和RESNET。 Voxceleb的实验结果表明，重量量化对于压缩SV模型有效。模型大小可以多次减小，而不会明显的性能降解。重新NET的压缩显示出比具有较低宽度量化的ECAPA-TDNN更强的结果。对层重量的分析表明，Resnet的平滑重量分布可能与其更好的鲁棒性有关。量化模型的概括能力通过语言不匹配的SV任务验证。此外，通过信息探测的分析表明，量化模型可以保留原始模型学到的大多数与说话者相关的知识。
DNN-based speaker verification (SV) models demonstrate significant performance at relatively high computation costs. Model compression can be applied to reduce the model size for lower resource consumption. The present study exploits weight quantization to compress two widely-used SV models, namely ECAPA-TDNN and ResNet. Experimental results on VoxCeleb show that weight quantization is effective for compressing SV models. The model size can be reduced multiple times without noticeable degradation in performance. Compression of ResNet shows more robust results than ECAPA-TDNN with lower-bitwidth quantization. Analysis of the layer weights suggests that the smooth weight distribution of ResNet may be related to its better robustness. The generalization ability of the quantized model is validated via a language-mismatched SV task. Furthermore, analysis by information probing reveals that the quantized models can retain most of the speaker-relevant knowledge learned by the original models.
