The ΔF method of vocal tract length normalization for vowels

IF 1.3 2区 文学 0 LANGUAGE & LINGUISTICS
Keith Johnson
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引用次数: 13

Abstract

Given the acoustic consequences of physiological differences between talkers, there is a practical need for effective and theoretically motivated procedures of vowel normalization to facilitate comparison of speech produced by people who differ by dialect or language. In addition, there is a question whether listeners might utilize a normalization procedure during speech perception. This paper reports the results of two studies that explore these questions—with particular focus on vocal tract length normalization. Drawing on research in speech engineering, where accurate estimates of vocal tract length are needed in some approaches to automatic speech recognition and speaker verification, a new model of vowel normalization is introduced. The model uses a direct measure of average formant spacing (the ΔF) which can be used to measure vocal tract length. The acoustic consequences of vocal tract length differences are removed from vowel measurements by scaling vowel formant measurements by ΔF. Study 1 found that this method is comparable to Nearey’s (1978) uniform normalization method, while providing an explicit vocal tract length interpretation, and a rationalized unit of measure. Study 2 found that uniform normalization measures (which let each formant serve as a noisy estimator of ΔF) improve vowel classification even with only a couple of randomly selected vowel tokens. This suggests that vocal tract length normalization could be involved in speech perception.
元音声道长度归一化ΔF方法
考虑到说话者之间生理差异的声学后果,实际需要有效的、理论上有动机的元音规范化程序,以促进方言或语言不同的人所产生的语音的比较。此外,还有一个问题是听者是否可能在语音感知过程中使用标准化过程。本文报告了两项研究的结果,探讨了这些问题,特别关注声道长度标准化。针对语音工程中一些自动语音识别和说话人验证方法需要准确估计声道长度的问题,提出了一种新的元音归一化模型。该模型使用平均形成峰间距(ΔF)的直接测量,可用于测量声道长度。通过ΔF缩放元音形成峰测量来消除声道长度差异的声学后果。研究1发现,该方法可与Nearey(1978)的均匀归一化方法相媲美,同时提供了明确的声道长度解释和合理化的度量单位。研究2发现,即使只有几个随机选择的元音标记,统一的归一化措施(让每个形成峰作为ΔF的噪声估计器)也可以改善元音分类。这表明声道长度归一化可能与语音感知有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.00
自引率
6.70%
发文量
17
审稿时长
8 weeks
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