Better HMM-Based Articulatory Feature Extraction with Context-Dependent Model

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The majority of speech recognition systems today commonly use Hidden Markov Models (HMMs) as acoustic models in systems since they can powerfully train and map a speech utterance into a sequence of units. Such systems perform even better if the units are context-dependent. Analogously, when HMM techniques are applied to the problem of articulatory feature extraction, context-dependent articulatory features should definitely yield a better result. This paper shows a possible strategy to extend a typical HMM-based articulatory feature extraction system into a context-dependent version which exhibits higher accuracy.