Understanding speech in the presence of other speech: Perceptual mechanisms for auditory scene analysis in human listeners

在存在其他语音的情况下理解语音：人类听众听觉场景分析的感知机制

• 批准号: ES/K004905/1
• 负责人: Brian Roberts
• 金额: $ 45.47万
• 依托单位: Aston University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FK004905%2F1
• 关键词: Understanding; speech; presence; other; Perceptual

We take it for granted that we can converse with other people in daily life and be understood with little, if any, noticeable effort. However, it is fairly unusual to hear the speech of a particular talker in isolation; speech is typically heard in the presence of interfering sounds, such as the voices of other talkers. The human auditory system, which is responsible for our sense of hearing, therefore faces the challenge of identifying which parts of the sounds reaching our ears have originated from the same environmental source. This involves separating those sound elements coming from one source (e.g., the voice of one talker) from those arising from other sources, and grouping them in ways that can be interpreted by higher-level processes in the brain (such as those involved in our understanding of speech). Without a solution to this "auditory scene analysis" problem, our perceptions of speech (and other sounds) would not correspond to the events that produced them. Humans have been exposed to a variety of complex listening environments over the course of evolution, and so we are generally very successful at understanding the speech of one person in the presence of other talkers. This contrasts with attempts to develop listening machines, which often fail catastrophically when confronted with complex listening environments, such as an open-plan office or a crowded party. Human listeners with hearing impairment also find these environments very difficult, even when using the latest developments in hearing-aid or cochlear-implant technology.So far, most research on auditory scene analysis has focussed on relatively simple sounds and has identified a number of general principles for the perceptual grouping and separation of sound elements. However, at least as currently understood, these principles seem inadequate to explain fully the perceptual grouping of speech. This is because the speech signal consists of a diverse and rapidly changing stream of sounds. The speech of our native language is also a highly familiar stimulus, and so by adulthood our auditory system has had many years to learn about its speech-specific properties. These properties may also assist in the successful perceptual grouping of speech.Much of the information necessary to understand speech is carried by the changes in frequency over time of a few broad peaks in the frequency spectrum of the speech signal, known as formants. The aim of this project is to investigate how human listeners presented with speech sound mixtures are able to group together the appropriate formants, and to reject others, such that the speech of the talker we want to listen to can be understood. We will do so using perceptual experiments with human listeners, in which we measure how the intelligibility of target speech (measured, for example, as the number of words reported correctly) changes under a variety of conditions. The project will explore the roles of general-purpose grouping factors (i.e., those that apply to a wide variety of sounds) and of speech-specific grouping factors, including higher-level constraints associated with the articulation of speech (i.e., the way our tongue, lips, and jaw move when we speak) and with the rules of our language. Our approach is to generate artificial speech-like stimuli with precisely controlled properties, to mix target speech with carefully designed "competitors" that offer alternative grouping possibilities, and to measure how manipulating the properties of these competitors affects the ability of human listeners to recognise the target speech in the mixture. The results of this project will not only improve our understanding of how human listeners separate speech from interfering sounds, but will also help to refine computer models of listening. Such refinements will in turn provide ways of improving the performance of devices such as hearing aids and automatic speech recognisers when they operate in noisy environments.

我们理所当然地认为，我们可以在日常生活中与其他人交谈，并且无需付出任何明显的努力就可以被理解。然而，单独听到某个特定讲话者的讲话是相当不寻常的。通常在存在干扰声音的情况下听到语音，例如其他说话者的声音。因此，负责我们听觉的人类听觉系统面临着识别到达我们耳朵的声音的哪些部分源自同一环境源的挑战。这涉及将来自一个来源（例如，一个说话者的声音）的声音元素与其他来源产生的声音元素分开，并以可以由大脑中更高级别的过程（例如涉及我们的大脑的过程）解释的方式对它们进行分组。言语理解）。如果没有解决这个“听觉场景分析”问题，我们对语音（和其他声音）的感知将不会与产生它们的事件相对应。人类在进化过程中已经接触到了各种复杂的聆听环境，因此我们通常能够非常成功地理解一个人在其他说话者在场时的讲话。这与开发监听机器的尝试形成鲜明对比，当面对复杂的监听环境（例如开放式办公室或拥挤的聚会）时，监听机器常常会遭遇灾难性的失败。即使使用助听器或人工耳蜗技术的最新发展，有听力障碍的人类听众也会发现这些环境非常困难。到目前为止，大多数有关听觉场景分析的研究都集中在相对简单的声音上，并确定了一些通用的声音声音元素的感知分组和分离的原则。然而，至少按照目前的理解，这些原则似乎不足以完全解释语音的感知分组。这是因为语音信号由多样化且快速变化的声音流组成。我们的母语语音也是一种非常熟悉的刺激，因此到成年后，我们的听觉系统已经有很多年的时间来了解其语音特定的属性。这些特性还可能有助于成功地对语音进行感知分组。理解语音所需的大部分信息是由语音信号频谱中一些宽峰（称为共振峰）的频率随时间的变化所承载的。该项目的目的是研究向人类听众呈现语音混合物如何能够将适当的共振峰组合在一起，并拒绝其他共振峰，以便能够理解我们想要听的说话者的语音。我们将使用人类听众的感知实验来实现这一点，其中我们测量目标语音的清晰度（例如，根据正确报告的单词数来测量）在各种条件下如何变化。该项目将探讨通用分组因素（即适用于各种声音的因素）和特定于语音的分组因素的作用，包括与语音清晰度相关的更高级别的约束（即我们的发音方式）当我们说话时，舌头、嘴唇和下巴会移动）并遵循我们的语言规则。我们的方法是生成具有精确控制属性的类似人工语音的刺激，将目标语音与精心设计的“竞争对手”混合，提供替代分组的可能性，并测量操纵这些竞争对手的属性如何影响人类听众识别语音的能力。混合物中的目标语音。该项目的结果不仅将提高我们对人类听众如何将语音与干扰声音分开的理解，而且还将有助于完善计算机的听力模型。这些改进反过来将提供提高助听器和自动语音识别器等设备在嘈杂环境中运行时性能的方法。

项目成果

Acoustic source characteristics, across-formant integration, and speech intelligibility under competitive conditions.

竞争条件下的声源特性、跨共振峰集成和语音清晰度。

• DOI: http://dx.10.1037/xhp0000038
• 发表时间: 2015
• 期刊: Journal of experimental psychology. Human perception and performance
• 作者: Roberts B

Informational masking and the effects of differences in fundamental frequency and fundamental-frequency contour on phonetic integration in a formant ensemble

信息掩蔽以及基频和基频轮廓的差异对共振峰合奏中语音整合的影响

• DOI: http://dx.10.1121/1.4949932
• 发表时间: 2016
• 期刊: Journal of the Acoustical Society of America
• 影响因子: 2.4
• 作者: Summers R

Informational masking of speech depends on masker spectro-temporal variation but not on its coherence.

语音的信息掩蔽取决于掩蔽谱时间变化，但不取决于其一致性。

• DOI: http://dx.10.1121/10.0002359
• 发表时间: 2020
• 期刊: The Journal of the Acoustical Society of America
• 作者: Roberts B

Informational masking and the effects of differences in fundamental frequency and fundamental-frequency contour on phonetic integration in a formant ensemble.

信息掩蔽以及基频和基频轮廓的差异对共振峰合奏中语音整合的影响。

• DOI: http://dx.10.1016/j.heares.2016.10.026
• 发表时间: 2017
• 期刊: Hearing research
• 影响因子: 2.8
• 作者: Summers RJ

Informational masking of monaural target speech by a single contralateral formant.

单个对侧共振峰对单耳目标语音的信息掩蔽。

• DOI: http://dx.10.1121/1.4919344
• 发表时间: 2015
• 期刊: The Journal of the Acoustical Society of America
• 作者: Roberts B