Padma Ramesh

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An important aspect of distinctive feature based approaches to automatic speech recognition is the formulation of a framework for robust detection of these features. We discuss the application of the support vector machines (SVM) that arise when the structural risk minimization principle is applied to such feature detection problems. In particular, we(More)
We consider the possibility of incorporating distinctive features into a statistically based speech recognizer. We develop a two pass strategy for recognition with a standard HMM based first pass followed by a second pass that performs an alternative analysis to extract class-specific features. For the voiced/voiceless distinction on stops for an alphabet(More)
Utterance verification is used in spoken language dialog systems to reject the speech that does not belong to the task and to correctly recognize the sentences that do. Current verification systems use context dependent (CD) or context independent (CI) subword models and CI anti-subword models. We propose many methods of modeling the CD anti-subword models.(More)
We examine the distinctive feature [voice] that separates the voiced from the unvoiced sounds for the case of stop consonants. We conduct acoustic-phonetic analyses on a large database and demonstrate the superior separability using a temporal measure (voice onset time; VOT) rather than spectral measures. We describe several algorithms to estimate the VOT(More)
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