Dysarthria is a motor speech disorder characterized by weakness,
paralysis, or poor coordination of the muscles responsible for
speech. Although automatic speech recognition (ASR) systems have
been developed for disordered speech, factors such as low
intelligibility and limited phonemic repertoire decrease speech
recognition accuracy. Furthermore, conventional speaker adaptation
algorithms that improve normal speech recognition may not perform
as well on dysarthric speakers. Instead of adapting the system, two
main techniques are proposed to model the pronunciation errors made
by the speaker: (1) a set of discrete hidden markov models (termed
as "metamodels") that incorporate a model of the speaker's phonetic
confusion-matrix into the ASR process; and (2) a network of
Weighted Finite-State Transducers (WFSTs) at the confusion-matrix,
word and language levels. These error modelling techniques attempt
to correct the errors made at the phonetic level and make use of a
language model to find the best estimate of the correct word
sequence. Hence, these techniques when integrated into the speech
recognition process performed significant error correction and
improved speech recognition
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