置信度评分引导的增量式和说话人自适应伪标注用于半监督老年人语音识别
Confidence Score Guided Incremental and Speaker Adaptive Pseudo-Labeling for Semi-Supervised Elderly Speech Recognition
AI总结：
提出一种置信度评分引导的增量式和说话人自适应伪标注方法，通过渐进式高质量伪标签选择和说话人自适应训练，在老年人语音识别中分别降低词错误率1.45%和字符错误率2.27%。
摘要：
本文提出了一种新颖的置信度评分引导的增量式和说话人自适应伪标注方法，用于半监督老年人语音识别。该方法促进了更高质量的伪标签选择和渐进式优化，同时减轻了说话人异质性。设计了一个置信度估计模块来对未转录数据的可靠性进行排序，从而实现从高置信度到低置信度逐步引入未标记数据子集的课程学习轨迹。通过带有可学习提示的说话人自适应训练来捕获说话人特定特征。在英语DementiaBank Pitt和粤语JCCOCC MoCA老年人语音数据集上的实验表明，所提出的方法相比不使用置信度评分引导的增量式或说话人自适应伪标注的半监督基线，在词错误率（WER）或字符错误率（CER）上取得了统计显著的降低，绝对降低分别为1.45%和2.27%（相对降低6.21%和6.98%）。
英文摘要：
This paper proposes a novel confidence score guided incremental and speaker adaptive pseudo-labeling approach for semi-supervised elderly speech recognition. It facilitates higher-quality pseudo-label selection and progressive refinement, while also mitigating speaker heterogeneity. A confidence estimation module is designed to rank the reliability of untranscribed data, enabling a curriculum learning trajectory that progressively folds in unlabeled data subsets from high to low confidence. Speaker-specific characteristics are captured through speaker adaptive training with learnable prompts. Experiments on the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets suggest that the proposed method outperforms the semi-supervised baseline using no confidence scores guided incremental or speaker adaptive pseudo-labeling by statistically significant word error rate (WER) or character error rate (CER) reductions of 1.45% and 2.27% absolute (6.21% and 6.98% relative).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
