CAREER: Breaking the phonetic code: novel acoustic-lexical modeling techniques for robust automatic speech recognition

职业：打破语音密码：用于鲁棒自动语音识别的新颖声学词汇建模技术

• 批准号: 0643901
• 负责人: Eric Fosler-Lussier
• 金额: $ 50.3万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-12-15 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0643901&HistoricalAwards=false
• 关键词: CAREER; Breaking; phonetic; code; novel

Spontaneous speech, accented speech, and speech in noise continue to provide automatic speech recognition (ASR) technology with significant challenges; error rates of ASR systems are still unacceptably high for these types of speech. This project establishes a consistent framework that seeks to cope with all of these conditions. The novel approach to phonetic variability investigated here views the problem as one of phonetic information underspecification: some subset of information that the listener receives will be missing or uncertain. Lexical access is thus a phonetic code-breaking problem --- how can a system accumulate phonetic cues in each of these conditions to recognize words on the basis of incomplete evidence? The research program of this project takes a multidisciplinary approach to integrating linguistic theory with speech recognition technology; discriminative statistical models of linguistic features are employed to model nonlinear, overlapping phonological effects observed in speech. The framework allows derivation of new linguistic insights through analysis of trained systems. The educational program fosters interdisciplinary research (with cross-disciplinary graduate seminars) and increases participation of underrepresented students in Computer Science by introducing language technology topics early into the undergraduate curriculum and encouraging undergraduate research. Apart from cultivating a new way of thinking about pronunciation variation for ASR, the broader impacts of this research are to provide collaborative resources for the ASR and linguistics communities to discuss in tutorial and workshop settings. Addressing noise, accent, and speaking style in a consistent framework will also improve ASR technology for many who are underserved by current systems.

自然语音、重音语音和噪声中的语音继续给自动语音识别（ASR）技术带来重大挑战；对于这些类型的语音，ASR 系统的错误率仍然高得令人无法接受。 该项目建立了一个一致的框架，旨在应对所有这些条件。这里研究的语音变异的新颖方法将问题视为语音信息不规范之一：听者收到的某些信息子集将丢失或不确定。因此，词汇访问是一个语音密码破解问题——系统如何在这些条件下积累语音线索，以便在不完整的证据的基础上识别单词？ 该项目的研究计划采用多学科方法将语言学理论与语音识别技术相结合；语言特征的判别统计模型被用来模拟在语音中观察到的非线性、重叠的语音效应。 该框架允许通过分析经过训练的系统得出新的语言见解。 该教育计划通过在本科课程中尽早引入语言技术主题并鼓励本科生研究来促进跨学科研究（通过跨学科研究生研讨会）并增加代表性不足的学生对计算机科学的参与。 除了培养一种新的 ASR 发音变化思考方式之外，这项研究更广泛的影响是为 ASR 和语言学界提供协作资源，以便在教程和研讨会中进行讨论。 在一致的框架中解决噪音、口音和说话风格问题也将改善许多当前系统服务不足的人的 ASR 技术。