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Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions

机译:基于新的频段重要性函数的在嘈杂条件下预测语音清晰度的客观措施

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摘要

The articulation index (AI), speech-transmission index (STI), and coherence-based intelligibility metrics have been evaluated primarily in steady-state noisy conditions and have not been tested extensively in fluctuating noise conditions. The aim of the present work is to evaluate the performance of new speech-based STI measures, modified coherence-based measures, and AI-based measures operating on short-term (30 ms) intervals in realistic noisy conditions. Much emphasis is placed on the design of new band-importance weighting functions which can be used in situations wherein speech is corrupted by fluctuating maskers. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train, and street interferences). Of all the measures considered, the modified coherence-based measures and speech-based STI measures incorporating signal-specific band-importance functions yielded the highest correlations (r=0.89-0.94). The modified coherence measure, in particular, that only included vowel/consonant transitions and weak consonant information yielded the highest correlation (r=0.94) with sentence recognition scores. The results from this study clearly suggest that the traditional AI and STI indices could benefit from the use of the proposed signal- and segment-dependent band-importance functions.
机译:清晰度指数(AI),语音传输指数(STI)和基于连贯性的清晰度指标已在稳态噪声条件下进行了评估,而在波动的噪声条件下并未进行广泛的测试。本工作的目的是评估在现实的嘈杂条件下,以短期(30 ms)间隔运行的新的基于语音的STI措施,改进的基于相干性的措施以及基于AI的措施的性能。在新的频带重要性加权函数的设计上有很多重点,这些函数可用于语音由于掩蔽器的波动而损坏的情况。通过正常听觉者在72种嘈杂条件下获得的可懂度评分对提议的措施进行评估,这些条件包括受四种不同掩蔽物(汽车,ba车声,火车和街道干扰)破坏的受噪声抑制的语音(辅音和句子)。在考虑的所有措施中,结合信号特定的频段重要性函数的基于相干性的改进措施和基于语音的STI措施产生了最高的相关性(r = 0.89-0.94)。尤其是仅包含元音/辅音过渡和弱辅音信息的改进的相干性度量与句子识别分数相关性最高(r = 0.94)。这项研究的结果清楚地表明,传统的AI和STI指数可能会受益于所建议的信号和片段相关的波段重要性函数的使用。

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