ACOUSTIC CORRELATES OF PHONETIC PERCEPTION

如果我们有一个可以解决的实际和理论问题 更好地理解语音背后的听觉机制 承认。这项建议旨在提高我们对 这些机制，特别侧重于元音感知。虽然 用光谱模式表示元音有很长的传统 越来越多的文献表明，在一个单独的时间片上采样， 动态特性在元音识别中起着重要的作用。尽管 这方面的文献，对其确切机制知之甚少。 涉及将动态光谱线索映射到感知的元音 质量。一些拟议的实验将检验具体的假设。 关于这种映射可能发生的方式。这些实验将使 使用由150个说话者说出的元音(男人， 妇女和儿童)。基频(F0)和 来自这些信号的共振峰轮廓将用于一系列实验 旨在确定F0、元音持续时间和音谱所起的作用 元音识别的变化。具体的假设将通过以下方式进行检验： (1)对150个说话者数据库中的令牌进行声学分析，以及(2) 涉及各种刺激的听力测试是从这些 代币。该项目的第二个目标是评估“蒙面峰” 表示“(MPR)，一种新的表示语音的方法，它是 作为传统共振峰表示法和 “全光谱”表示法。共振峰表示法被广泛使用 因为它们可以解释相对大量的研究结果 语音知觉。共振峰理论的主要弱点是 跟踪自然语音中的共振峰是一个困难且本质上 未解决的问题。在很大程度上是为了应对这个问题，一些 研究人员提出了一种全谱方法，其中语音 质量由整体光谱形状控制。全谱图 然而，这种方法不能解释非常令人信服的数据表明 语音质量的判断主要受语音频率的影响 光谱峰值，相对不受光谱形状细节的影响 非峰区。MPR的设计目的是保持对 光谱峰值，但不需要显式跟踪共振峰。 MPR背后的基本思想是：(1)获得与基音无关的 倒谱平滑频谱，(2)刺激非线性听觉 通过计算树皮尺度变换进行频率编码，以及(3)仿真 通过减去谱值的移动平均值来进行横向抑制。 由此产生的“屏蔽光谱”保留了光谱峰值，但去除了大部分 其他光谱形状细节。MPR的评估标准如下：(1) 基于MPR的感知语音距离预测的实验比较 与更传统的听觉模型相比，(2)语音识别 使用隐马尔可夫模型将MPR频谱序列映射到 要么是语音分段的单词，要么是(3)语音听力测试 由MPR波谱重新合成。

Numerous practical and theoretical problems could be addressed if we had a better understanding of the auditory mechanisms underlying phonetic recognition. This proposal is aimed at improving our understanding of these mechanisms, with a particular focus on vowel perception. Although there is a long tradition of representing vowels by the spectral pattern sampled at a single time slice, a growing body of literature suggests that dynamic properties play an important role in vowel identification. Despite this literature, relatively little is known about the precise mechanisms that are involved in mapping dynamic spectral cues onto perceived vowel quality. Some of the proposed experiments will test specific hypotheses about the way in which this mapping might occur. The experiments will make use of a large database consisting of vowels spoken by 150 talkers (men, women, and children). Measurements of fundamental frequency (F0) and formant contours from these signals will be used in a series of experiments designed to determine the role played by F0, vowel duration, and spectral change in vowel identification. Specific hypotheses will be tested by: (1) acoustic analysis of tokens in the 150-talker database, and (2) listening tests involving various kinds of stimuli resynthesized from these tokens. A second goal of this project is to evaluate the "Masked Peak Representation" (MPR), a new method of representing speech which was developed as an alternative to both traditional formant representations and "whole spectrum" representations. Formant representations are widely used because these they can account for a relatively large number of findings in phonetic perception. The principal weakness of formant theory is that tracking formants in natural speech is a difficult and essentially unresolved problem. Largely in response to this problem, some investigators have proposed a whole spectrum approach in which phonetic quality is controlled by overall spectral shape. The whole spectrum approach, however, cannot account for very convincing data showing that judgments of phonetic quality are affected primarily by the frequencies of spectral peaks, and relatively unaffected by spectral shape details in nonpeak regions. The MPR was designed to retain maximal sensitivity to spectral peaks but without requiring the explicit tracking of formants. The basic idea behind the MPR is to: (1) obtain a pitch-independent spectrum through cepstral smoothing, (2) stimulate nonlinear auditory frequency coding by computing a bark-scale transform, and (3) simulate lateral suppression by subtracting a running average of spectral values. The resulting "masked spectrum" retains spectral peaks but removes most other spectral shape details. The MPR will be evaluated with: (1) an experiment comparing MPR-based predictions of perceived phonetic distance with those of a more traditional auditory model, (2) speech recognition tests that use a Hidden Markov Model to map sequences of MPR spectra onto either words of phonetic segments, and (3) listening tests with speech resynthesized from MPR spectra.