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—An effective way to increase the noise robustness of automatic speech recognition is to label noisy speech features as either reliable or unreliable (missing), and to replace (impute) the missing ones by clean speech estimates. Conventional im-putation techniques employ parametric models and impute the missing features on a frame-by-frame basis. At low(More)
Model-based techniques for robust speech recognition often require the statistics of noisy speech. In this paper, we propose two modifications to obtain more accurate versions of the statistics of the combined HMM (starting from a clean speech and a noise model). Usually, the phase difference between speech and noise is neglected in the acoustic environment(More)
We describe an algorithm to automatically estimate the voice onset time (VOT) of plosives. The VOT is the time delay between the burst onset and the start of periodicity when it is followed by a voiced sound. Since the VOT is affected by factors like place of articulation and voicing it can be used for inference of these factors. The algorithm uses the(More)