A Model for Determining Consonantal Features in Speech

确定语音辅音特征的模型

• 批准号: 6399547
• 负责人: KENNETH N STEVENS
• 金额: $ 46.3万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-05-01 至 2006-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6399547
• 关键词: behavioral /social science research tag; biological models; clinical research; cues; hearing disorders; human subject; model design /development; neural information processing; noise; phonology; sound; sound perception; speech; speech disorders; speech recognition; vocabulary

The long-range goal of this project is to develop a model that simulates the human perception of the words in spoken language. The immediate goal in the present project is to achieve one part of this task: the identification of the distinctive features of consonants in spoken syllables, words and sentences. Key ideas in our approach are that the speech signal contains reliable acoustic cues to the articulation implemented by the speaker even when surface phonetics are variable, and that the articulation is governed by abstract contrastive phonological representations. Identification of the consonant features proceeds by first performing an acoustic analysis of the speech to establish the locations of landmarks or discontinuities in the sound where consonant closures and releases are formed. The sound in the vicinity of those landmarks is then subjected to further detailed analysis to establish the underlying features of the consonant that generates each landmark, including the place of articulation, the voicing feature, the nasal feature etc. This further analysis involves extracting from the sound a number of attributes that provide cues for each of the underlying consonant features. The selection of these attributes is guided by the requirement that they be closely related to the articulatory shapes and movements that produced the speech. In the proposed work, our current understanding of the combination of attributes that most effectively reveal the articulation and its governing features and segments will be expanded and refined, through detailed theory-driven acoustic measures, perceptual experimentation and appropriate statistical analysis. The robustness of the model will be evaluated using various kinds of utterances, from citation forms to running speech. The performance of the model will also be tested in speech that has been contaminated with noise, and the errors made by the model will be compared with those made by human listeners. The model has application in the study of speech perception by listeners with impaired hearing or by listeners in an environment in which speech is degraded. Understanding of these processes of speech perception can lead to improved approaches to the remediation of disorders of speech perception and production.

该项目的长期目标是开发一个模型，模拟人类对口语中单词的感知。 本项目的近期目标是实现这一任务的一部分：识别口语音节、单词和句子中辅音的显著特征。 在我们的方法中的关键思想是，语音信号包含可靠的声学线索，即使当表面语音是可变的，由扬声器实现的发音，和发音是由抽象的对比语音表示。 辅音特征的识别通过首先执行语音的声学分析来进行，以建立形成辅音闭合和释放的声音中的界标或不连续的位置。 然后对这些标志附近的声音进行进一步的详细分析，以建立产生每个标志的辅音的基本特征，包括发音的位置、发声特征、鼻音特征等。这种进一步的分析涉及从声音中提取许多属性，这些属性为每个基本辅音特征提供线索。 这些属性的选择是由这些属性与产生语音的发音形状和运动密切相关的要求指导的。 在拟议的工作中，我们目前的理解，最有效地揭示了清晰度及其管理功能和段的属性组合将扩大和完善，通过详细的理论驱动的声学措施，感性实验和适当的统计分析。 该模型的鲁棒性将使用各种话语进行评估，从引用形式到运行语音。 该模型的性能也将在被噪声污染的语音中进行测试，并将该模型所产生的错误与人类听众所产生的错误进行比较。 该模型在听力受损的听众或在语音退化的环境中的听众的语音感知的研究中有应用。 了解这些过程的言语知觉可以导致改善的方法来补救的障碍，言语知觉和生产。