用贝塔-二叉分布描述部分信用语音识别评分中的相关性。

IF 1.2 Q3 ACOUSTICS JASA express letters Pub Date : 2024-02-01 DOI:10.1121/10.0024633
Adam K Bosen
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引用次数: 0

摘要

语音识别任务的部分信用评分可以提高测量精度。然而,由于有意义的语音包含上下文线索,而上下文线索会在正确识别刺激中每个标记的概率之间产生相关性,因此评估部分信用评分的改进幅度具有挑战性。在此,我们使用贝塔二叉分布来估算人工耳蜗听者(N = 20)对单词中的音素和句子中的单词的识别准确率和类内相关性。估算结果表明,刺激内的识别准确率存在很大的类内相关性。这些相关性在不同个体之间是不变的。在对部分信用评分进行功率分析时,应考虑类内相关性。
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Characterizing correlations in partial credit speech recognition scoring with beta-binomial distributions.

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.

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The JIBO Kids Corpus: A speech dataset of child-robot interactions in a classroom environment. The perceptual distinctiveness of the [n-l] contrast in different vowel and tonal contexts. Ambient noise source characterization using spectral, coherence, and directionality estimates at Kongsfjorden. Speaker adaptation using codebook integrated deep neural networks for speech enhancement. Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech.
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