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    Method, apparatus, and radio for optimizing hidden markov model speech recognition
    25.
    发明专利
    Method, apparatus, and radio for optimizing hidden markov model speech recognition 未知

    公开(公告)号：AU681058B2

    公开(公告)日：1997-08-14

    申请号：AU5353196

    申请日：1996-01-29

    Applicant: MOTOROLA INC

    Inventor： HARTMAN MATTHEW A ,  KUSHNER WILLIAM M ,  SRENGER EDWARD

    IPC: G10L15/14 ,  G10L9/00

    Abstract: In a statistical based speech recognition system, one of the key issues is the selection of the Hidden Markov Model that best matches a given sequence of feature observations. The problem is usually addressed by the calculation of the maximum likelihood, ML, state sequence by means of a Viterbi or other decoder. Noise or inadequate training can produce a ML sequence associated with a Hidden Markov Model other than the correct model. The method of the present invention provides improved robustness by combining the standard ML state sequence score (416) with an additional path score (418) derived from the dynamics of the ML score as a function of time. These two scores, when combined, form a hybrid metric (420) that, when used with the decoder, optimizes selection of the correct Hidden Markov Model (422).

    Method, apparatus, and radio for optimizing hidden markov mo del speech recognition
    26.
    发明专利
    Method, apparatus, and radio for optimizing hidden markov mo del speech recognition 未知

    公开(公告)号：AU5353196A

    公开(公告)日：1996-10-16

    申请号：AU5353196

    申请日：1996-01-29

    Applicant: MOTOROLA INC

    Inventor： HARTMAN MATTHEW A ,  KUSHNER WILLIAM M ,  SRENGER EDWARD

    IPC: G10L15/14 ,  G10L9/00

    Abstract: In a statistical based speech recognition system, one of the key issues is the selection of the Hidden Markov Model that best matches a given sequence of feature observations. The problem is usually addressed by the calculation of the maximum likelihood, ML, state sequence by means of a Viterbi or other decoder. Noise or inadequate training can produce a ML sequence associated with a Hidden Markov Model other than the correct model. The method of the present invention provides improved robustness by combining the standard ML state sequence score (416) with an additional path score (418) derived from the dynamics of the ML score as a function of time. These two scores, when combined, form a hybrid metric (420) that, when used with the decoder, optimizes selection of the correct Hidden Markov Model (422).

    Vector quantizer method and apparatus
    27.
    发明专利
    Vector quantizer method and apparatus 未知

    公开(公告)号：GB2282943A

    公开(公告)日：1995-04-19

    申请号：GB9422823

    申请日：1994-03-07

    Applicant: MOTOROLA INC

    Inventor： GERSON IRA A ,  JASIUK MARK A ,  HARTMAN MATTHEW A

    IPC: G10L19/06 ,  G10L19/083 ,  G10L19/135 ,  G10L25/06 ,  G10L25/24 ,  G10L9/00

    Abstract: A Vector-Sum Excited Linear Predictive Coding (VSELP) speech coder (200) provides improved quality and reduced complexity over a typical speech coder. VSELP uses a codebook (201) which has a predefined structure such that the computations required for the codebook search process can be significantly reduced. This VSELP speech coder uses single or multisegment vector quantizer of the reflection coefficients based on a Fixed-Point-Lattice-Technique (FLAT). Additionally, this speech coder uses a pre-quantizer to reduce the vector codebook search complexity and a high-resolution scalar quantizer to reduce the amount of memory needed to store the reflection coefficient vector codebooks. Resulting in a high quality speech coder with reduced computations and storage requirements.

    Vector sum excited linear predictive coding speech coder
    28.
    发明专利
    Vector sum excited linear predictive coding speech coder 未知

    公开(公告)号：FR2709387A1

    公开(公告)日：1995-03-03

    申请号：FR9410203

    申请日：1994-08-23

    Applicant: MOTOROLA INC

    Inventor： GERSON IRA A ,  JASIUK MARK A ,  HARTMAN MATTHEW A

    IPC: G10L19/06 ,  G10L19/083 ,  G10L19/135 ,  G10L25/06 ,  G10L25/24 ,  H04B7/24 ,  G10L9/14 ,  G10L9/18 ,  H04B7/00

    Abstract: The method comprises the steps of segmenting the optimal reflection coefficient vector into two segments, and providing an array of predetermined vectors of coefficients, with each vector having multiple elements. A first vector is selected from the array. The residual error is calculated corresp. to the first selected vector. The above steps are repeated for each vector in the array. A vector is chosen from the first array with the lowest residual error, which defines the initial conditions for the second segment. A second array of predetermined vectors of reflection coefficients are provided, each vector having multiple elements. The above steps are repeated for the second segment, using the second array of vectors so forming a second chosen vector.

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