用独立预训练模型实现高效文本相关语音验证，省去联合微调开销。
Memory-Efficient Training for Text-Dependent SV with Independent Pre-trained Models
- 分离使用两个预训练模型，避免联合微调复杂性。
- 在评测集上达最小决策代价0.0358，排名第一。
- 适合资源受限场景下的高精度语音验证应用。
本文提交至2024年伊朗文本相关说话人验证挑战赛（TdSV）。传统方法通常联合建模说话人与语言特征，需在未分段输入下训练，计算成本高。且常对大规模预训练说话人嵌入模型在目标数据集上微调，可能损害其捕捉说话人特性的能力。为此，我们采用两个独立预训练模型的系统，通过针对性领域自适应，在避免传统方法中未分段输入联合微调高成本的同时，仍取得良好性能。最佳系统在评测子集上达到最小决策代价（MinDCF）0.0358，获得挑战赛第一名。
原文摘要 · Abstract (English)
This paper presents our submission to the Iranian division of the Text-Dependent Speaker Verification Challenge (TdSV) 2024. Conventional TdSV approaches typically jointly model speaker and linguistic features, requiring unsegmented inputs during training and incurring high computational costs. Additionally, these methods often fine-tune large-scale pre-trained speaker embedding models on the target domain dataset, which may compromise the pre-trained models' original ability to capture speaker-specific characteristics. To overcome these limitations, we employ a TdSV system that utilizes two pre-trained models independently and demonstrate that, by leveraging pre-trained models with targeted domain adaptation, competitive results can be achieved while avoiding the substantial computational costs associated with joint fine-tuning on unsegmented inputs in conventional approaches. Our best system reached a MinDCF of 0.0358 on the evaluation subset and secured first place in the challenge.
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