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USING ARTICULATORY KNOWLEDGE IN AUTOMATIC SPEECH RECOGNITION

Over the years researchers have proposed various solutions to the problems of automatic speech recognition. As a result, different types of speech recognizers have been developed. During the last decade it has appeared that speech recognizers based on hidden Markov models (HMMs) usually have a better performance than other types of speech recognizers, like e.g. rule-based speech recognizers. Probably this is due to a combination of several factors. Two important differences between rule-based and HMM-based recognizers are (1) that the first use mainly deterministic rules while the latter use a kind of stochastic rules; and (2) that in rule-based recognizers local decisions are often made (i.e. whether a segment is voiced or not), while in HMM-based systems one overall probabilistic decision is made.