ACOUSTIC CORRELATES OF PHONETIC PERCEPTION

如果我们有一个 更好地理解语音背后的听觉机制 识别. 这一建议旨在提高我们对以下问题的认识： 这些机制，特别关注元音感知。 虽然 用频谱模式来表示元音有着悠久的传统 越来越多的文献表明， 动态特性在元音识别中起重要作用。 尽管 在这篇文献中，对精确的机制知之甚少 参与将动态频谱线索映射到感知的元音 质量. 一些拟议中的实验将测试特定的假设 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（男性， 妇女和儿童）。 基频（F0）和 这些信号的共振峰轮廓将用于一系列实验 旨在确定F0、元音持续时间和频谱所起的作用 元音识别的变化。 具体假设将通过以下方式进行检验： (1)150个说话者数据库中的标记的声学分析，以及（2） 听力测试涉及各种刺激重新合成从这些 代币 该项目的第二个目标是评估“掩蔽峰 表示”（MPR），一种新的语音表示方法， 作为传统共振峰表示的替代方案， “全谱”表征。 Forbidden表示法被广泛使用 因为它们可以解释相对大量的发现， 语音知觉 共振峰理论的主要弱点是， 在自然语音中跟踪共振峰是一个困难的， 未解决的问题。 为了解决这个问题，一些 研究人员提出了一种全谱方法， 质量由整个光谱形状控制。 整个频谱 然而，这种方法无法解释非常令人信服的数据， 语音质量的判断主要受 光谱峰值，并且相对不受光谱形状细节的影响， 非高峰地区。 MPR的设计是为了保持对 频谱峰值，但不需要共振峰的显式跟踪。 MPR背后的基本思想是：（1）获得与音高无关的 频谱通过倒谱平滑，（2）刺激非线性听觉 通过计算巴克尺度变换进行频率编码，以及（3）模拟 通过减去频谱值的移动平均值来进行横向抑制。 由此产生的“掩蔽光谱”保留了光谱峰值，但去除了大部分 其他光谱形状细节。 MPR将通过以下方式进行评价：（1） 比较基于MPR的感知语音距离预测的实验 与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 语音片段的单词，以及（3）语音听力测试 从MPR光谱重新合成。

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.