Backing-off Context- & Gender-dependent Models for Better Articulatory Feature Extraction
نویسندگان
چکیده
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 employed are context-dependent and gender-dependent. Analogously, when HMM technology is applied to the problem of articulatory feature extraction, contextand gender-dependent articulatory features should definitely yield a better result. This paper presents a possible strategy which utilizes the strength of contextand gender-dependent models to build a better HMM-based articulatory feature extraction system.
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