In the current study, we explore the factors underlying the well-known difficulty in acoustic classification of front nonsibilant fricatives (Maniwa, Jongman & Wade 2009, McMurray & Jongman 2011) by applying a novel classification method to the production of Greek speakers. The Greek fricative inventory [f v θ ð s z ç ʝ x ɣ] includes voiced and voiceless segments from five distinct places of articulation. Our corpus contains all of the Greek fricatives produced by 29 monolingual speakers, but our focus is on the distinction between the front nonsibilant fricatives [f v θ ð]. For comparison, we also discuss the other places of articulation where relevant. We apply a relatively novel classification method based on cepstral coefficients, previously successful in categorizing English obstruent bursts (Bunnell, Polikoff & McNicholas 2004), English vowels (Ferragne & Pellegrino 2010), Romanian fricatives (Spinu & Lilley 2016), and Russian fricatives (Spinu, Kochetov & Lilley 2018). For this study, fricative boundaries were automatically aligned using Hidden Markov Models (HMMs) and then manually checked. Six Bark-frequency cepstral coefficients (c0–c5) were extracted from 20-millisecond Hann windows. HMMs were used to divide the fricatives and adjacent vowels into three regions of internally minimized variance. A multinomial logistic regression analysis then used the mean cepstral coefficients from each region as predictors for classification by consonant identity. Our method yields highly successful classification rates, exceeding the performance of previous methods. We discuss these results in light of the differences of the phonemic distributions of fricatives between English and Greek.

中文翻译：

---

探索希腊语的前摩擦音对比：基于倒谱系数的声学变异性研究

在目前的研究中，我们通过将一种新颖的分类方法应用于希腊语扬声器的制作，探索了众所周知的前非摩擦音声学分类困难的因素（Maniwa, Jongman & Wade 2009, McMurray & Jongman 2011）。希腊擦音清单 [fv θ ð sz ç ʝ x ɣ] 包括来自五个不同发音位置的浊音和清音片段。我们的语料库包含 29 位单语使用者产生的所有希腊擦音，但我们的重点是区分前非擦音 [fv θ ð]。为了比较，我们还讨论了其他相关的发音位置。我们应用了一种相对新颖的分类方法，基于倒谱系数, 之前成功地分类了英语阻塞爆发 (Bunnell, Polikoff & McNicholas 2004)、英语元音 (Ferragne & Pellegrino 2010)、罗马尼亚擦音 (Spinu & Lilley 2016) 和俄语擦音 (Spinu, Kochetov & Lilley 2018)。在本研究中，摩擦音边界使用自动对齐隐马尔可夫模型（HMM），然后手动检查。从 20 毫秒的 Hann 窗口中提取了六个 Bark 频率倒谱系数 (c0-c5)。HMM 用于将摩擦音和相邻元音分成三个内部最小方差的区域。然后，多项逻辑回归分析使用来自每个区域的平均倒谱系数作为辅音同一性分类的预测因子。我们的方法产生了非常成功的分类率，超过了以前方法的性能。我们根据英语和希腊语擦音音位分布的差异来讨论这些结果。