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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)
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)
Maintaining a high level of robustness for Automatic Speech Recognition (ASR) systems is especially challenging when the background noise has a time-varying nature. We have implemented a Model-Based Feature Enhancement (MBFE) technique that not only can easily be embedded in the feature extraction module of a recogniser, but also is intrinsically suited for(More)