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Multistream Recognition of Speech: Dealing With Unknown Unknowns

机译:语音的多流识别:处理未知未知数

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The paper discusses an approach for dealing with unexpected acoustic elements in speech. The approach is motivated by observations of human performance on such problems, which indicate the existence of multiple parallel processing streams in the human speech processing cognitive system, combined with the human ability to know when the correct information is being received. Some earlier relevant engineering approaches in multistream automatic recognition of speech (ASR) that aimed at processing of noisy speech and at dealing with unexpected out-of-vocabulary words are reviewed. The paper also reviews some currently active research in multistream ASR, focusing mainly on feedback-based techniques involving fusion of information between individual processing streams. The difference between the system behavior on its training data and during its operation is proposed as a substitute for the human ability of “knowing when knowing.” Most recent results indicate 9% relative improvement in error rates in phoneme recognition of high signal-to-noise ratio speech and as high as 30% relative improvements in moderate noise.
机译:本文讨论了一种处理语音中意外的声学元素的方法。该方法是通过观察人类对此类问题的表现来激发的,这表明人类语音处理认知系统中存在多个并行处理流,并结合了人类知道何时接收到正确信息的能力。审查了一些早期的有关语音多流自动识别(ASR)的相关工程方法,该方法旨在处理嘈杂的语音并处理意外的语音外单词。本文还回顾了当前在多流ASR中的一些活跃研究,主要关注基于反馈的技术,这些技术涉及各个处理流之间的信息融合。有人建议将系统在其训练数据上的行为与运行期间的行为之间的差异替换为人类“知道时知道”的能力。最新的结果表明,在高信噪比语音的音素识别中,错误率的相对改善为9%,在中等噪声中的相对改善高达30%。

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