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English(EN) Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR

新分析使用 F-TDNN 改进儿童语音识别

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员分析了自动语音识别 (ASR) 系统的微调策略，特别关注涉及儿童语音的低资源场景。该研究调查了各种声学特征及其对不同声学模型的影响，发现音高特征显著提高了构音障碍语音的识别性能。通过系统地检查带有因子化时延神经网络 (F-TDNN) 模型的 TORGO 数据库，该团队在孤立词识别方面取得了 4.65% 的相对改进，在句子识别方面取得了 4.63% 的相对改进。 AI

影响 这项研究可能带来更准确的儿童语音识别系统，特别是针对有语言障碍的儿童。

排序理由 该集群包含一篇学术论文，详细介绍了语音识别的新分析和方法。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新分析使用 F-TDNN 改进儿童语音识别

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该集群包含一篇学术论文，详细介绍了语音识别的新分析和方法。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Paban Sapkota, Hemant Kumar Kathania, Mikko Kurimo, Sudarsana Reddy Kadiri, Shrikanth Narayanan ·

    跨数据集、年龄和性别泛化：低资源儿童语音识别微调策略综合分析

    arXiv:2606.19791v1 Announce Type: cross Abstract: The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has demonstrated improved recognition through the use o…

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