Enhancing speech intelligibility in reverberant spaces by a speech features distributions dependent pre-processing

Mzah, Yosra; Chaoui, Sandra

doi:10.1007/s10772-018-9536-3

Cited by 1 publication

(2 citation statements)

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“…It also affects the shape of formants. The authors of [ 23 ] similarly describe the impact of reverberation. They point out that the degradation of speech caused by reverberation is related to the phenomenon called overlap masking—parts of speech are masked by reverberant components of the preceding segment.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Sensing Analytics Applied to Speech in Reverberation Conditions

Odya

Kotus

Kurowski

et al. 2021

Sensors

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The paper aims to discuss a case study of sensing analytics and technology in acoustics when applied to reverberation conditions. Reverberation is one of the issues that makes speech in indoor spaces challenging to understand. This problem is particularly critical in large spaces with few absorbing or diffusing surfaces. One of the natural remedies to improve speech intelligibility in such conditions may be achieved through speaking slowly. It is possible to use algorithms that reduce the rate of speech (RoS) in real time. Therefore, the study aims to find recommended values of RoS in the context of STI (speech transmission index) in different acoustic environments. In the experiments, speech intelligibility for six impulse responses recorded in spaces with different STIs is investigated using a sentence test (for the Polish language). Fifteen subjects with normal hearing participated in these tests. The results of the analytical analysis enabled us to propose a curve specifying the maximum RoS values translating into understandable speech under given acoustic conditions. This curve can be used in speech processing control technology as well as compressive reverse acoustic sensing.

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Section: Introductionmentioning

confidence: 99%

“…The experiments (involving syllables in Japanese) showed that subjects performed better while listening to the processed speech. This method was then developed by Mzah et al [ 23 ]. The main difference was the modification of the steady-state detection technique.…”

Section: Introductionmentioning

confidence: 99%