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For noise-vocoded sentences, a manipulation that removes spectral detail from speech, listeners' reporting improved from near 0% to 70% correct over 30 sentences (Experiment 1). Learning was enhanced if listeners heard distorted sentences while they knew the identity of the undistorted target (Experiments 2 and 3). Learning was absent when listeners were trained with nonword sentences (Experiments 4 and 5), although the meaning of the training sentences did not affect learning (Experiment 5). Perceptual learning of noise-vocoded speech depends on higher level information, consistent with top-down, lexically driven learning. Similar processes may facilitate comprehension of speech in an unfamiliar accent or following cochlear implantation. Humans are able to understand speech in a variety of situations that dramatically affect the sounds that reach their ears. They can understand talkers with quite different (foreign or regional) accents, who speak at different speeds, in rooms that introduce reverberation, or when the speech is conveyed over low-fidelity devices such as the telephone. The robustness of speech comprehension to many forms of variation and distortion is currently unmatched by computer speech recognition systems and may therefore reflect a unique specialization of the human perceptual system. Experimental work has demonstrated that speech perception remains robust even when challenged with extreme forms of artificial distortion. For example, speech remains understandable when formants are resynthesized as sinusoids , a manipulation that removes most of the natural qualities of the human voice from a speech signal. Other manipulations have shown that dramatic alterations to both the temporal and spectral properties of speech do not substantially impair the intelligibility of spoken language when background noise is absent. It is unlikely that there is any single set of acoustic properties or cues that are preserved in all of these different forms of distorted yet still-intelligible speech (cf. . Therefore, robustness in speech perception reflects the multiple acoustic means by which stable elements of speech (such as phonetic features or syllables) are coded in clear speech: This redundancy permits comprehension when any single cue is lost. Robustness in speech comprehension may also derive from the operation of compensatory mechanisms that are recruited when speech becomes difficult to understand: processes of adaptation and perceptual learning are two such mechanisms. The human language system can dynamically adapt to variation in the acoustic realization of speech, tuning the perceptual system so as to optimally process the current speech input. These adaptation processes can take place very rapidly. For example, adaptation to natural changes in speech rate (e.g.,","grobid_abstract_attachment_id":"89359807"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"MRC Cognition and Brain Sciences Unit","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [47847299]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card ds-work-card--no-bottom-spacing"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":89359807,"attachmentType":"pdf"}"><img alt="First page of “MRC Cognition and Brain Sciences Unit”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/89359807/mini_magick20220807-1-7t900e.png?1659901540" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">MRC Cognition and Brain Sciences Unit</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="47847299" href="https://independent.academia.edu/IJohnsrude"><img alt="Profile image of Ingrid Johnsrude" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/47847299/14978817/15725171/s65_ingrid.johnsrude.jpg" />Ingrid Johnsrude</a></div><div class="ds-work-card--detail"><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">20 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 84283310; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Speech comprehension is resistant to acoustic distortion in the input, reflecting listeners' ability to adjust perceptual processes to match the speech input. For noise-vocoded sentences, a manipulation that removes spectral detail from speech, listeners' reporting improved from near 0% to 70% correct over 30 sentences (Experiment 1). Learning was enhanced if listeners heard distorted sentences while they knew the identity of the undistorted target (Experiments 2 and 3). Learning was absent when listeners were trained with nonword sentences (Experiments 4 and 5), although the meaning of the training sentences did not affect learning (Experiment 5). Perceptual learning of noise-vocoded speech depends on higher level information, consistent with top-down, lexically driven learning. Similar processes may facilitate comprehension of speech in an unfamiliar accent or following cochlear implantation. Humans are able to understand speech in a variety of situations that dramatically affect the sounds that reach their ears. They can understand talkers with quite different (foreign or regional) accents, who speak at different speeds, in rooms that introduce reverberation, or when the speech is conveyed over low-fidelity devices such as the telephone. The robustness of speech comprehension to many forms of variation and distortion is currently unmatched by computer speech recognition systems and may therefore reflect a unique specialization of the human perceptual system. Experimental work has demonstrated that speech perception remains robust even when challenged with extreme forms of artificial distortion. For example, speech remains understandable when formants are resynthesized as sinusoids , a manipulation that removes most of the natural qualities of the human voice from a speech signal. Other manipulations have shown that dramatic alterations to both the temporal and spectral properties of speech do not substantially impair the intelligibility of spoken language when background noise is absent. It is unlikely that there is any single set of acoustic properties or cues that are preserved in all of these different forms of distorted yet still-intelligible speech (cf. . Therefore, robustness in speech perception reflects the multiple acoustic means by which stable elements of speech (such as phonetic features or syllables) are coded in clear speech: This redundancy permits comprehension when any single cue is lost. Robustness in speech comprehension may also derive from the operation of compensatory mechanisms that are recruited when speech becomes difficult to understand: processes of adaptation and perceptual learning are two such mechanisms. The human language system can dynamically adapt to variation in the acoustic realization of speech, tuning the perceptual system so as to optimally process the current speech input. These adaptation processes can take place very rapidly. 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