Interference in spoken communication: Evaluating the corrupting and disrupting effects of other voices

在日常生活中，与其他人交谈不仅对分享知识和想法很重要，而且对保持对社区的归属感也很重要。大多数人想当然地认为，他们可以毫不费力地与人交谈。成功的沟通包括理解所说的和被理解的内容，但很少有人能单独听到某个人的讲话。说话通常是在有干扰声的情况下听到的，这些干扰声通常是其他说话者的声音。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来自哪个说话者的挑战。解决这个“听觉场景分析”问题需要将来自一个来源的声音元素（例如，你正在听的谈话者的声音）与来自其他来源的声音元素分离开来，这样目标来源的身份和意义就可以被大脑中更高层次的过程解释。在进化的过程中，人类已经暴露在各种复杂的倾听环境中，所以我们通常非常成功地理解一个人在其他说话人面前的讲话。这与开发监听机器的尝试形成了鲜明对比，后者在遇到不利条件时经常失败，比如在开放式办公室里自动转录一段对话。听力受损的人类听众经常发现，即使使用最新发展的助听器或人工耳蜗设计，这种环境也特别困难，因此很难在这种条件下进行有效沟通。理解语音所必需的大部分信息（声学-语音信息）是由语音信号频谱中几个宽峰的频率随时间的变化所携带的，这些峰被称为共振峰。该项目旨在研究听众如何将目标语音和干扰共振峰混合在一起，将合适的共振峰组合在一起，并拒绝其他共振峰，从而能够理解我们想要听的说话者的讲话。干扰声音有两种效果——能量掩蔽，即耳朵对目标的神经反应被对掩蔽物的反应淹没；信息掩蔽，即“听觉大脑”无法将目标的易于检测的部分与掩蔽物分开。该项目将探索干扰的信息掩蔽成分-通常是限制语音可理解性的主要因素-使用消除能量掩蔽的刺激配置。我们将使用感知实验来测量我们理解语言的能力（例如，正确报告的单词数量）在各种条件下的变化。该项目将研究声学-语音信息如何在共振峰之间组合。它还将探讨类语音干扰如何影响可理解性，区分干扰占用一些可用感知处理能力的情况和干扰的特定属性侵入目标语音感知的情况。我们的方法是使用具有精确控制特性的人工类语音刺激，将目标语音与精心设计的干扰物混合在一起，提供不同的分组可能性，并测量操纵这些干扰物的特性如何影响听者在混合中识别目标语音的能力。研究结果将提高我们对人类听众如何将语音与干扰声音区分开来的理解，以及这种区分的限制，有助于完善听力的计算模型。这样的改进将反过来提供改善诸如助听器和自动语音识别器等设备在不利听力条件下工作时的性能的方法。

In everyday life, talking with other people is important not only for sharing knowledge and ideas, but also for maintaining a sense of belonging to a community. Most people take it for granted that they can converse with others with little or no effort. Successful communication involves understanding what is being said and being understood, but it is quite rare to hear the speech of a particular talker in isolation. Speech is typically heard in the presence of interfering sounds, which are often 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 come from which talker.Solving this "auditory scene analysis" problem involves separating those sound elements arising from one source (e.g., the voice of the talker to whom you are attending) from those arising from other sources, so that the identity and meaning of the target source can be interpreted by higher-level processes in the brain. Over the course of evolution, humans have been exposed to a variety of complex listening environments, 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 when confronted with adverse conditions, such as automatic transcription of a conversation in an open-plan office. Human listeners with hearing impairment often find these environments especially difficult, even when using the latest developments in hearing-aid or cochlear-implant design, and so can struggle to communicate effectively in such conditions.Much of the information necessary to understand speech (acoustic-phonetic information) 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 project aims to investigate how listeners presented with mixtures of target speech and interfering formants 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. Interfering sounds can have two kinds of effect - energetic masking, in which the neural response of the ear to the target is swamped by the response to the masker, and informational masking, in which the "auditory brain" fails to separate readily detectable parts of the target from the masker. The project will explore the informational masking component of interference - often the primary factor limiting speech intelligibility - using stimulus configurations that eliminate energetic masking. We will do so using perceptual experiments in which we measure how our ability to understand speech (e.g., the number of words reported correctly) changes under a variety of conditions.The project will examine how acoustic-phonetic information is combined across formants. It will also explore how a speech-like interferer affects intelligibility, distinguishing the circumstances in which the interferer takes up some of the available perceptual processing capacity from those in which specific properties of the interferer intrude into the perception of the target speech. Our approach is to use artificial speech-like stimuli with precisely controlled properties, to mix target speech with carefully designed interferers that offer alternative grouping possibilities, and to measure how manipulating the properties of these interferers affects listeners' abilities to recognise the target speech in the mixture. The results will improve our understanding of how human listeners separate speech from interfering sounds and the constraints on that separation, helping to refine computational 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 adverse listening conditions.