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用贝塔二项式分布刻画部分信用语音识别评分中的相关性。

Characterizing correlations in partial credit speech recognition scoring with beta-binomial distributions.

机构信息

Center for Hearing Research, Boys Town National Research Hospital, Omaha, Nebraska 68131,

出版信息

JASA Express Lett. 2024 Feb 1;4(2). doi: 10.1121/10.0024633.

Abstract

Partial credit scoring for speech recognition tasks can improve measurement precision. However, assessing the magnitude of this improvement with partial credit scoring is challenging because meaningful speech contains contextual cues, which create correlations between the probabilities of correctly identifying each token in a stimulus. Here, beta-binomial distributions were used to estimate recognition accuracy and intraclass correlation for phonemes in words and words in sentences in listeners with cochlear implants (N = 20). Estimates demonstrated substantial intraclass correlation in recognition accuracy within stimuli. These correlations were invariant across individuals. Intraclass correlations should be addressed in power analysis of partial credit scoring.

摘要

部分计分在语音识别任务中可以提高测量精度。然而,使用部分计分评估这种改进的程度具有挑战性,因为有意义的语音包含语境线索,这会在刺激中每个标记的正确识别概率之间产生相关性。在这里,使用β-二项式分布来估计人工耳蜗植入者(N=20)对单词中的音位和句子中的单词的识别准确率和组内相关系数。结果表明,在刺激内的识别准确率具有很高的组内相关系数。这些相关性在个体之间是不变的。在部分计分的功效分析中应该考虑组内相关系数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b46/10848658/2963a5df079d/JELAAE-000004-025202_1-g001.jpg

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