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    Communication device and method for screening speech recognizer input
    21.
    发明专利
    Communication device and method for screening speech recognizer input 未知

    公开(公告)号：GB2346001B

    公开(公告)日：2001-03-07

    申请号：GB0000918

    申请日：2000-01-14

    Applicant: MOTOROLA INC

    Inventor： POLIKAITIS AUDRIUS ,  KUSHNER WILLIAM M

    IPC: G10L11/02 ,  G10L15/22 ,  G10L15/20

    Abstract: A communication device capable of screening speech recognizer input includes a microprocessor (110) connected to communication interface circuitry (115), memory (120), audio circuitry (130), an optional keypad (140), a display (150), and a vibrator/buzzer (160). Audio circuitry (130) is connected to microphone (133) and speaker (135). Microprocessor (110) includes a speech/noise classifier and speech recognition technology. Microprocessor (110) analyzes a speech signal to determine speech waveform parameters within a speech acquisition window. Microprocessor (110) compares the speech waveform parameters to determine whether an error exists in the signal format of the speech signal. Microprocessor (110) informs the user when an error exists in the signal format and instructs the user how to correct the signal format to eliminate the error.

    Communication device and method for screening speech recognizer input
    22.
    发明专利
    Communication device and method for screening speech recognizer input 未知

    公开(公告)号：GB2346001A

    公开(公告)日：2000-07-26

    申请号：GB0000918

    申请日：2000-01-14

    Applicant: MOTOROLA INC

    Inventor： POLIKAITIS AUDRIUS ,  KUSHNER WILLIAM M

    IPC: G10L11/02 ,  G10L15/22 ,  G10L15/20

    Abstract: A communication device capable of screening speech recognizer input includes a microprocessor (110) connected to communication interface circuitry (115), memory (120), audio circuitry (130), an optional keypad (140), a display (150), and a vibrator/buzzer (160). Audio circuitry (130) is connected to microphone (133) and speaker (135). Microprocessor (110) includes a speech/noise classifier and speech recognition technology. Microprocessor (110) analyzes a speech signal to determine speech waveform parameters within a speech acquisition window. Microprocessor (110) compares the speech waveform parameters to determine whether an error exists in the signal format of the speech signal. Microprocessor (110) informs the user when an error exists in the signal format and instructs the user how to correct the signal format to eliminate the error.

    METHOD, APPARATUS, AND RADIO FOR OPTIMIZING HIDDEN MARKOV MODEL SPEECH RECOGNITION
    24.
    发明专利
    METHOD, APPARATUS, AND RADIO FOR OPTIMIZING HIDDEN MARKOV MODEL SPEECH RECOGNITION 未知

    公开(公告)号：CA2189249A1

    公开(公告)日：1996-10-03

    申请号：CA2189249

    申请日：1996-01-29

    Applicant: MOTOROLA INC

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

    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 an 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 core (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 AND APPARATUS FOR SPEECH RECONSTRUCTION IN A DISTRIBUTED SPEECH RECOGNITION SYSTEM
    25.
    发明公开
    METHOD AND APPARATUS FOR SPEECH RECONSTRUCTION IN A DISTRIBUTED SPEECH RECOGNITION SYSTEM 有权
    方法和设备语音重建分布式语音识别系统

    公开(公告)号：EP1395978A4

    公开(公告)日：2005-09-21

    申请号：EP02709089

    申请日：2002-01-18

    Applicant: MOTOROLA INC

    Inventor： KUSHNER WILLIAM M ,  MEUNIER JEFFREY ,  JASIUK MARK A ,  RAMABADRAN TENKASI V

    IPC: G10L19/08 ,  G10L15/00 ,  G10L15/28

    CPC classification number: G10L15/30 ,  G10L19/00 ,  G10L19/093 ,  G10L25/18

    Abstract: A method of reconstructing speech input at a communication device comprises receiving, at the communication device, encoded data that includes encoded spectral data and encoded energy data of the speech input, the encoded spectral data being encoded as a series of mel-frequency cepstral coefficients. The method further comprises decoding, at the communication device, the encoded spectral data and encoded energy data to determine the spectral data and energy data, wherein decoding comprises: performing an inverse discrete cosine transform on the mel-frequency cepstral coefficients at harmonic mel-frequencies corresponding to a pitch period of the speech input to determine log-spectral magnitudes of the speech input at the harmonic mel-frequencies, and exponentiating the log-spectral magnitudes to determine the spectral magnitudes of the speech input. The method also comprises combining the spectral data and energy data to reconstruct the speech input at the communication device. A communication device for use in distributed speech recognition system is also disclosed.

    METHOD AND APPARATUS FOR CHARACTERIZING INHALATIONNOISE AND CALCULATING PARAMETERS BASED ON THE CHA RACTERIZATION
    28.
    发明申请
    METHOD AND APPARATUS FOR CHARACTERIZING INHALATIONNOISE AND CALCULATING PARAMETERS BASED ON THE CHA RACTERIZATION 审中-公开
    基于CHA赛车表征吸入排气和计算参数的方法和装置

    公开(公告)号：WO2006007342B1

    公开(公告)日：2006-05-04

    申请号：PCT/US2005020299

    申请日：2005-06-09

    Applicant: MOTOROLA INC ,  KUSHNER WILLIAM M ,  HARTON SARA M ,  JASIUK MARK A

    Inventor： KUSHNER WILLIAM M ,  HARTON SARA M ,  JASIUK MARK A

    IPC: G01L15/00 ,  G10L15/06 ,  G10L21/00

    CPC classification number: G10L17/26 ,  A62B18/08 ,  B63C11/26 ,  G10L15/20 ,  G10L21/0208

    Abstract: A method for characterizing inhalation noise within a pressurized air delivery system, the method including the steps of: generating an inhalation noise model (912, 1012) based on inhalation noise; receiving an input signal (802) that includes inhalation noise comprising at least one inhalation noise burst; comparing (810) the input signal to the noise model to obtain a similarity measure; comparing the similarity measure to at least one threshold (832, 834) to detect the at least one inhalation noise burst; and characterizing (1354, 1356) the at least one detected inhalation noise burst.

    Abstract translation: 一种用于表征加压空气输送系统内的吸入噪声的方法，所述方法包括以下步骤：基于吸入噪声产生吸入噪声模型（912,1012）; 接收包括至少一个吸入噪声突发的吸入噪声的输入信号（802）; 将输入信号与噪声模型进行比较（810）以获得相似性度量; 将所述相似性度量与至少一个阈值（832,834）进行比较以检测所述至少一个吸入噪声突发; 以及表征（1354,1356）所述至少一个检测到的吸入噪声突发。

    METHOD, APPARATUS, AND RADIO FOR OPTIMIZING HIDDEN MARKOV MODEL SPEECH RECOGNITION
    30.
    发明公开
    METHOD, APPARATUS, AND RADIO FOR OPTIMIZING HIDDEN MARKOV MODEL SPEECH RECOGNITION 失效
    方法，装置和无线为了获得最佳的语音识别借助HIDDEN MARKOFFMODELLE的

    公开(公告)号：EP0764319A4

    公开(公告)日：1998-12-30

    申请号：EP96910297

    申请日：1996-01-29

    Applicant: MOTOROLA INC

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

    IPC: G10L15/14 ,  G10L9/00

    CPC classification number: G10L15/142

    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 an 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 core (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).

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