标题： 基于Wav2vec2的假音频检测低秩自适应方法 显示英文标题

标题： Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

摘要： 自监督语音模型是假音频检测领域的一个快速发展研究课题。 许多预训练模型可以作为特征提取器，学习更丰富和更高层次的语音特征。 然而，在微调预训练模型时，通常会面临训练时间过长和内存消耗高的挑战，并且完全微调也非常昂贵。 为了解决这个问题，我们将低秩适应（LoRA）应用于wav2vec2模型，冻结预训练模型权重，并在Transformer架构的每一层中注入一个可训练的秩分解矩阵，大大减少了下游任务的可训练参数数量。 与在包含317M训练参数的wav2vec2模型上使用Adam进行微调相比，LoRA通过将可训练参数的数量减少198倍，实现了相似的性能。 显示英文摘要

摘要： Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times.