Prosodic phrasing detection using acoustic source characteristics

使用声源特征进行韵律短语检测

• 批准号: 386546-2010
• 负责人: Yoon, TaeJin
• 金额: $ 0.36万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=507856
• 关键词: Prosodic; phrasing; detection; using; acoustic

The goal of this multidisciplinary research among linguistics and speech technology is to develop an efficient algorithm for prosodic phrase prediction using voice source characteristics. The human voice has evolved as a vehicle for conveying many different types of information, and human listeners have developed the ability to detect very small and subtle voice quality changes, and interpret their function. Our abilities lag far behind what the human ear can effortlessly do. Most of the studies to date have been very limited, either in the quantity of data analyzed, or in the kinds of source measures made. Advances in the field have been particularly hampered by the lack of availability of suitable analysis tools. Manual interactive techniques which has been generally adopted in the investigation to voice quality across languages permit a fine-grained analysis of the source, but because of their labour-intensive nature, are not suitable for the large scale studies that would be ideally needed for progress in this field (Epstein, 2002; Hardcastle & Laver, 1997; Ishi, Ishiguro, & Hagita, 2008). Therefore, it is an important task to develop an efficient tool for (semi-)automatic prosodic phrasing annotation, which are becoming more and more important for the speech community. For example, the prediction of prosodic phrase boundaries is an important task for natural language processing to convey the correct meaning and text-to-speech synthesis applications to increase the naturalness of the synthesized speech. It has been reported that the prosodic boundary is occasionally glottalized, but because glottalization is only occasional, and because glottalization is difficult to characterize using standard speech signal processing methods, this acoustic feature of prosodic phrasing has apparently not been extensively studied or incorporated into automatic prosodic phrasing prediction algorithm. Investigating prosody or computational modeling of prosodic phrase boundary through the study of these acoustic features is complicated by the fact that pitch, loudness, duration and voice quality is also affected by paralinguistic properties of the utterance such as the speaker's emotional state, and even by non-linguistic factors such as speaker's gender and age.

这项语言学和语音技术相结合的多学科研究的目标是开发一种利用语音源特征进行韵律短语预测的有效算法。人类的声音已经演变成一种传达许多不同类型信息的工具，人类听众已经发展出检测非常微小和微妙的语音质量变化并解释其功能的能力。我们的能力远远落后于人类耳朵可以毫不费力地做到的事情。到目前为止，大多数研究都非常有限，无论是在分析的数据量上，还是在来源测量的种类上。由于缺乏适当的分析工具，这一领域的进展尤其受到阻碍。人工交互技术通常被用于调查不同语言的语音质量，允许对源进行细粒度的分析，但由于其劳动密集型的性质，不适合这一领域的进步所理想地需要的大规模研究(Epstein，2002；HardCastle&Laver，1997；Ishi，Ishiguro，&Hagita，2008)。因此，开发一种高效的(半自动)韵律短语标注工具是一项重要的任务，因为韵律短语标注在语音界变得越来越重要。例如，韵律短语边界的预测是自然语言处理传达正确含义的重要任务，也是文本到语音合成应用中增加合成语音自然度的重要任务。据报道，韵律边界有时是声门化的，但由于声门化只是偶然的，而且由于声门化很难用标准的语音信号处理方法来表征，这种韵律短语的声学特征显然没有被广泛研究或纳入自动韵律短语预测算法。通过对这些声学特征的研究来研究韵律或韵律短语边界的计算模型是复杂的，因为基音、响度、持续时间和声音质量也受到话语的副语言属性(如说话人的情绪状态)的影响，甚至受到非语言因素(如说话人的性别和年龄)的影响。