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English(EN) Speaker Group Encoding in Self-supervised Speech Recognition Models

语音模型编码说话人的人口统计信息,影响公平性

一篇新的研究论文探讨了自监督语音识别模型如何编码说话人分组信息。研究发现,这些模型可以识别性别、年龄、方言、种族和母语者身份等特征。对模型进行说话人识别或自动语音识别(ASR)的微调会改变保留的说话人分组信息的类型,其中ASR微调会丢弃语音变异而保留语义变异。研究表明,这些发现可能有助于开发更公平的ASR算法。 AI

影响 研究结果有助于通过理解模型如何编码敏感的人口统计数据,从而构建更公平的ASR系统。

排序理由 该集群包含一篇详细介绍AI模型研究结果的学术论文。

在 arXiv cs.CL 阅读 →

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语音模型编码说话人的人口统计信息,影响公平性

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Felix Herron, Solange Rossato Alexandre Allauzen, Benoit Favre, Fran\c{c}ois Portet ·

    Speaker Group Encoding in Self-supervised Speech Recognition Models

    arXiv:2606.10654v1 Announce Type: new Abstract: We investigate what self-supervised speech recognition models (S3Ms) learn about speaker groups (SGs). We examine several states of S3Ms: pretrained, finetuned on speaker identification (SID), finetuned on automatic speech recogniti…

  2. arXiv cs.CL TIER_1 English(EN) · François Portet ·

    Speaker Group Encoding in Self-supervised Speech Recognition Models

    We investigate what self-supervised speech recognition models (S3Ms) learn about speaker groups (SGs). We examine several states of S3Ms: pretrained, finetuned on speaker identification (SID), finetuned on automatic speech recognition (ASR), and ASR-finetuned using a fairness enh…