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预测餐饮场所中言语交流质量的最佳条件。

Prediction of optimal conditions for verbal-communication quality in eating establishments.

机构信息

School of Environmental Health, University of British Columbia, 3rd Floor, 2206 East Mall, Vancouver, BC V6T1Z3, Canada.

出版信息

J Acoust Soc Am. 2011 Apr;129(4):2005-14. doi: 10.1121/1.3552867.

Abstract

This paper discusses the prediction of verbal-communication quality in eating establishments (EEs). EEs contain talkers and listeners who require high speech intelligibility at their tables, and high speech privacy between tables. Using catt-Acoustic, verbal-communication quality--quantified by speech transmission index (STI)--in models of three existing EEs was predicted. Talker voice-output levels were predicted using an existing empirical model accounting for the Lombard effect. With these, catt-Acoustic predicted impulse responses, speech levels and noise levels at primary and secondary listener positions, and the corresponding STIs. The untreated EEs were first modeled for various talker and listener positions, and occupancies. Then various treated configurations, involving reduced volume, increased absorption and barriers were studied to determine the effectiveness of the treatments. The results suggest that placing barriers around tables can be an effective way to achieve good verbal-communication quality. Increasing the absorption of the room surfaces or decreasing the ceiling height to control reverberation may not be effective. However, increasing the surface absorption and putting barriers around tables may achieve optimal speech conditions in EEs. Subdividing large EEs into smaller ones can also be effective.

摘要

本文讨论了餐饮场所(EEs)中言语交流质量的预测。EEs 中有说话者和聆听者,他们在餐桌上需要高言语可懂度,在桌子之间需要高言语隐私性。本文使用 catt-Acoustic,通过言语传输指数(STI)预测了三种现有 EE 模型中的言语交流质量。使用考虑到 Lombard 效应的现有经验模型预测了说话者的语音输出水平。在此基础上,catt-Acoustic 预测了主要和次要聆听者位置的脉冲响应、语音水平和噪声水平,以及相应的 STI。首先,对未经处理的 EEs 进行了各种说话者和聆听者位置和占用情况的建模。然后,研究了各种处理配置,包括降低音量、增加吸声和障碍物,以确定处理的效果。结果表明,在桌子周围设置障碍物可能是实现良好言语交流质量的有效方法。增加房间表面的吸声或降低天花板高度以控制混响可能效果不佳。然而,增加表面吸声并在桌子周围设置障碍物可能会在 EEs 中实现最佳的语音条件。将大的 EEs 细分为较小的 EEs 也可能有效。

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