1000-word speaker-independent continuous-speech recognition using hidden Markov models

An algorithm based on hidden Markov models is applied to the task of speaker-independent continuous-speech recognition for a vocabulary of 1000 words with no syntactic constraints. The signal is limited to 4000 Hz. Word models were built from three-state representations of phonetic units, concatenated according to entries in a lexicon. Performance as measured on DARPAs resource management database was 40% correct word recognition. It was found that the use of several different acoustic features and the use of word-specific phonetic modeling, where possible, improved system performance.<<ETX>>

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