人工耳蜗使用者的音素建模和开放集词识别:初步报告。

T A Meyer, S Frisch, M A Svirsky, D B Pisoni
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引用次数: 0

摘要

在对音素正确预测的基础上,我们得出结论,闭集特征识别可以成功地预测开集词识别任务中的音素识别。然而,对于单词识别,PCM模型低估了观察到的表现,并且需要添加一个心理词汇(即SPAMR模型)来很好地匹配7名患有CIs的成年人的平均数据。词汇的正确预测随着词汇的增加而提高,这为CI用户在开放集口语单词识别中使用词汇信息的假设提供了支持。对比CNCs更复杂的单词的感知也可能需要词汇知识(Frisch等人,本增刊,第60-62页)。在未来,我们将使用CI用户在心理物理任务上的表现来生成预测的元音和辅音混淆矩阵,用于预测开放集口语单词识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling phoneme and open-set word recognition by cochlear implant users: a preliminary report.

On the basis of the good predictions for phonemes correct, we conclude that closed-set feature identification may successfully predict phoneme identification in an open-set word recognition task. For word recognition, however, the PCM model underpredicted observed performance, and the addition of a mental lexicon (ie, the SPAMR model) was needed for a good match to data averaged across 7 adults with CIs. The predictions for words correct improved with the addition of a lexicon, providing support for the hypothesis that lexical information is used in open-set spoken word recognition by CI users. The perception of words more complex than CNCs is also likely to require lexical knowledge (Frisch et al, this supplement, pp 60-62) In the future, we will use the performance off individual CI users on psychophysical tasks to generate predicted vowel and consonant confusion matrices to be used to predict open-set spoken word recognition.

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