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

自发语音、重音语音和噪声中的语音继续给自动语音识别(ASR)技术带来巨大的挑战；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。这个项目建立了一个一致的框架，试图应对所有这些情况。本文研究的语音变异性的新方法将这个问题视为语音信息不足的问题：听者接收的信息的某些子集将丢失或不确定。因此，词汇获取是一个语音密码破译问题-一个系统如何在每个条件下积累语音线索，以便在不完整证据的基础上识别单词？该项目的研究项目采用了多学科的方法，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于模拟语音中观察到的非线性、重叠的语音效果。该框架允许通过分析训练有素的系统来获得新的语言学见解。该教育计划通过将语言技术主题尽早引入本科课程并鼓励本科生研究，促进跨学科研究(通过跨学科研究生研讨会)，并增加计算机科学专业中代表性不足的学生的参与。除了为ASR培养一种新的发音变化的思维方式外，这项研究的更广泛的影响是为ASR和语言学社区提供协作资源，供他们在教程和研讨会的环境中讨论。在一致的框架中解决噪音、口音和说话方式也将为当前系统服务不足的许多人改进ASR技术。

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.