利用TFS刺激恢复包络线索预测正常听者的语音感知

Khadija Akter, Nursadul Mamun
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引用次数: 1

摘要

言语感知是指听者对说话产生的可理解性和能力。为了解释语音信号中的语音信息,人类听觉系统同时使用包络(ENV)和时间精细结构(TFS)线索。虽然ENV足以在安静环境下理解语音,但TFS线索对于嘈杂环境下的语音隔离是必要的。言语线索可以分为两类;包膜(ENV)和颞精细结构(TFS)。一般来说,缓慢变化的ENV(称为恢复的ENV)可以从快速变化的TFS中恢复;然而,ENV的恢复程度及其对语音感知的意义尚不清楚。为了量化恢复的ENV对语音感知的相对贡献,本研究提出了一个新的语音感知度量。该指标采用了听觉外围的现象学模型来模拟听觉神经纤维对原始和恢复的ENV信号的反应。在不同声音呈现水平下,在不同类型的噪声(稳态和波动噪声)下评估了拟议度量的性能。最后,为了验证所提出的度量,将预测分数与行为研究的主观评价分数进行比较。与现有指标相比,提议的指标表明所有病例具有统计上显著的相关性,并且具有更宽的动态范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Speech Perception with Recovered Envelope Cues from TFS stimulus For Normal Hearing Listener
Speech perception refers to the understandability and ability of speech by a listener produced by a speaker. In order to interpret the speech information in the signal, human auditory system uses both envelope (ENV) and temporal fine structure (TFS) cues. While ENV is sufficient for understanding speech in quiet, TFS cues are necessary for speech segregation in noisy conditions. Speech cues can be classified into two categories; envelope (ENV) and temporal fine structure (TFS). In general, slow varying ENV (known as recovered ENV) can be recovered from the rapidly changing TFS; however, the degree of ENV recovery and its significance on speech perception are not clearly understood. In order to quantify the relative contribution of the recovered ENV for speech perception, this study proposes a new speech perception metric. The proposed metric employs a phenomenological model of the auditory periphery developed to simulate the responses of the auditory nerve fibers to both original and recovered ENV cues. The performance of the proposed metric was evaluated under different types of noise (both steady-state and fluctuating noise) at different sound presentation level. Finally, to validate the proposed metric, the predicted scores were compared with subjective evaluation scores from behavioral studies. The proposed metric indicates a statistically significant correlation for all cases and accounts for a wider dynamic range compared to the existing metrics.
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