Abstract
The constant frame length in typical ASR front ends is too long to capture transient phenomena in speech, such as stop bursts. However, current HMM systems have consistently outperformed systems based solely on non-uniform units. This work investigates an approach to add back such transient information to a speech recognizer, without losing the robustness of the standard acoustic models. We demonstrate a set of phonetically-motivated acoustic features that discriminate a preliminary test set of highly ambiguous voiceless stops in CV contexts. The features are automaticallycomputed from data that had been hand-marked for consonant burst location and voicing onset (extension to automatic marking is also proposed). […]
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