Suppr超能文献

语音被语音调制噪声打断的 glimpsing。

Glimpsing speech interrupted by speech-modulated noise.

机构信息

Department of Communication Sciences and Disorders, University of South Carolina, 1229 Marion Street, Columbia, South Carolina 29201, USA.

出版信息

J Acoust Soc Am. 2018 May;143(5):3058. doi: 10.1121/1.5038273.

Abstract

Everyday environments frequently present speech in modulated noise backgrounds, such as from a competing talker. Under such conditions, temporal glimpses of speech may be preserved at favorable signal-to-noise ratios during the amplitude dips of the masker. Speech recognition is determined, in part, by these speech glimpses. However, properties of the noise when it dominates the speech may also be important. This study interrupted speech to provide either high-intensity or low-intensity speech glimpses derived from measurements of speech-on-speech masking. These interrupted intervals were deleted and subsequently filled by steady-state noise or one of four different types of noise amplitude modulated by the same or different sentence. Noise was presented at two different levels. Interruption by silence was also examined. Speech recognition was best with high-intensity glimpses and improved when the noise was modulated by missing high-intensity segments. Additional noise conditions detailed significant interactions between the noise level and glimpsed speech level. Overall, high-intensity speech segments, and the amplitude modulation (AM) of the segments, are crucial for speech recognition. Speech recognition is further influenced by the properties of the competing noise (i.e., level and AM) which interact with the glimpsed speech level. Acoustic properties of both speech-dominated and noise-dominated intervals of speech-noise mixtures determine speech recognition.

摘要

日常环境中经常存在调制噪声背景下的语音,例如来自竞争说话者的语音。在这种情况下,在掩蔽器的幅度下降期间,有利的信噪比可能会保留语音的短暂 glimpses。语音识别部分取决于这些语音 glimpses。然而,当噪声主导语音时,噪声的特性也可能很重要。本研究通过语音对掩蔽的测量来中断语音,提供高强度或低强度的语音 glimpses。这些中断的间隔被删除,然后由稳态噪声或由相同或不同句子调制的四种不同类型的噪声的强度填充。噪声以两种不同的水平呈现。还研究了沉默中断。高强度 glimpses 的语音识别效果最佳,当噪声由缺失的高强度片段调制时,语音识别会得到改善。其他噪声条件详细说明了噪声水平和 glimpsed 语音水平之间的显著相互作用。总体而言,高强度语音片段和片段的幅度调制(AM)对于语音识别至关重要。竞争噪声的特性(即水平和 AM)进一步影响语音识别,与 glimpsed 语音水平相互作用。语音-噪声混合物的语音主导和噪声主导区间的声学特性决定了语音识别。

相似文献

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验