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English(EN) Beyond Word Error Rate: A Switch Aware Evaluation of ASR and Audio Language Models on English Yoruba Code-Switched Speech

新的评估方法揭示ASR/音频LM在英语-约鲁巴语语码转换方面存在困难

一篇新的研究论文介绍了一种感知切换的评估方法,用于自动语音识别(ASR)和音频语言模型(音频LM)在处理语码转换语音时的性能,特别关注英语和约鲁巴语。研究发现,标准的词错误率(WER)指标可能会掩盖语码转换场景下显著的性能差异。研究强调,尽管总体WER可能相似,但模型在约鲁巴语片段以及语言切换点上的表现要差得多,一些生成模型还表现出翻译或冗长的问题。 AI

影响 凸显了当前ASR和音频LM在低资源、语码转换语言上的关键性能限制,需要新的评估标准。

排序理由 一篇介绍ASR和音频LM新评估方法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的评估方法揭示ASR/音频LM在英语-约鲁巴语语码转换方面存在困难

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一篇介绍ASR和音频LM新评估方法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Chibuzor Okocha, Christan Earl Grant ·

    超越词错误率:在英-约鲁巴语混合语音上对ASR和音频语言模型进行感知切换的评估

    arXiv:2609.11786v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems and audio language models (audio LMs) now report low error rates on monolingual benchmarks, but their behavior on code switched speech in low resource, diacritic rich languages remains poor…