SPEECH-RECOGNITION CIRCUITRY EMPLOYING NONLINEAR PROCESSING, SPEECH ELEMENT MODELING AND PHONEME ESTIMATION

    公开(公告)号:CA2023424A1

    公开(公告)日:1991-02-18

    申请号:CA2023424

    申请日:1990-08-16

    Applicant: ELIZA CORP

    Abstract: A phoneme estimator in a speech-recognition system includes energy detect circuitry for detecting the segments of a speech signal that should be analyzed for phoneme content. Speech-element processors then process the speech signal segments, calculating nonlinear representations of the segments. The nonlinear representation data is applied to speech-element modeling circuitry which reduces the data through speech element specific modeling. The reduced data are then subjected to further nonlinear processing. The results of the further nonlinear processing are again applied to speechelement modeling circuitry, producing phoneme isotype estimates. The phoneme isotype estimates are rearranged and consolidated, that is, the estimates are uniformly labeled and duplicate estimates are consolidated, forming estimates of words or phrases containing minimal numbers of phonemes. The estimates may then be compared with stored words or phrases to determine what was spoken.

    SPEECH-RECOGNITION CIRCUITRY EMPLOYING PHONEME ESTIMATION

    公开(公告)号:CA1329272C

    公开(公告)日:1994-05-03

    申请号:CA563601

    申请日:1988-04-08

    Applicant: ELIZA CORP

    Abstract: A phoneme estimator (12) in a speech-recognition system (10) includes trigger circuitry (18, 22) for identifying the segments of speech that should be analyzed for phoneme content. Speech-element processors (24, 26, and 28) calculate the likelihoods that currently received speech contains individual phonemes, but they operate only when the trigger circuitry identifies such segments. The computation-intensive processing for determining phoneme likelihoods is thus performed on only a small subset of the received speech segments. The accuracy of the speechelement processors (24, 26, and 28) is enhanced because these processors operate by recognition of patterns not only in elements of the data-reduced representations of the received speech but also in higher-ordered products of those elements; that is, these circuits employ non-linear modeling for phoneme identification.

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