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Towards Improving Intelligibility of Black-Box Speech Synthesizers in Noise

机译：致力于提高黑盒语音合成器在噪声中的清晰度

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This paper explores how different synthetic speech systems can be understood in a noisy environment that resembles radio noise. This work is motivated by a need for intelligible speech in noisy environments such as emergency response and disaster notification. We discuss prior work done on listening tasks as well as speech in noise. We analyze three different speech synthesizers in three different noise settings. We measure quantitatively the intelligibility of each synthesizer in each noise setting based on human performance on a listening task. Finally, treating the synthesizer and its generated audio as a black box, we present how word level and sentence level input choices can lead to increased or decreased listener error rates for synthesized speech.
机译：本文探讨了在类似于无线电噪声的嘈杂环境中如何理解不同的合成语音系统。这项工作的动力是在嘈杂的环境中（例如紧急响应和灾难通知）需要清晰的语音。我们讨论了在听音任务以及噪音下所做的先前工作。我们在三种不同的噪声设置下分析了三种不同的语音合成器。我们根据人类在听音任务上的表现，定量测量每种合成器在每种噪声设置下的清晰度。最后，将合成器及其生成的音频视为黑匣子，我们介绍了单词级别和句子级别的输入选择如何导致合成语音的听众错误率提高或降低。

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