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

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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.