Literature DB >> 17471719

Measurement and prediction of speech and noise levels and the Lombard effect in eating establishments.

Murray Hodgson1, Gavin Steininger, Zohreh Razavi.   

Abstract

Measurements made of the acoustical characteristics of, and occupied noise levels in, ten eating establishments are described. Levels to which diners and employees were exposed varied from 45 to 82 dB(A). From these levels and diner questionnaire responses, the number of customers present and average noise levels to which individual diners were exposed during their visits were estimated. These data, assumptions about the number of talkers per customer, and classical room-acoustical theory were used to deduce talker voice output levels. These varied from slightly above "casual" to "loud." An iterative model for predicting speech and noise levels in eating establishments, including the Lombard effect as described by a new, proposed model, was developed. With the measured noise levels as the target for prediction, optimization techniques were used to find best estimates of unknown prediction parameters--such as those defining the Lombard effect, the number of talkers per customer, and the average absorption per customer--with highly credible results. The prediction algorithm and optimal parameters constitute a novel model for predicting speech and noise levels--and thus speech intelligibility--in eating establishments, as a function of the number of customers, including a proven, realistic model of the Lombard effect.

Mesh:

Year:  2007        PMID: 17471719     DOI: 10.1121/1.2535571

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  13 in total

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8.  Ambulatory monitoring of Lombard-related vocal characteristics in vocally healthy female speakers.

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Journal:  J Acoust Soc Am       Date:  2020-06       Impact factor: 1.840

9.  Talker Versus Dialect Effects on Speech Intelligibility: A Symmetrical Study.

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10.  Audiomotor Perceptual Training Enhances Speech Intelligibility in Background Noise.

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