公开(公告)号：US6067515A

公开(公告)日：2000-05-23

申请号：US957903

申请日：1997-10-27

申请人： Lin Cong ,  Safdar M. Asghar

发明人： Lin Cong ,  Safdar M. Asghar

IPC分类号： G10L15/02 ,  G10L15/10 ,  G10L15/20 ,  G10L9/00

CPC分类号： G10L15/02 ,  G10L15/10 ,  G10L15/20

摘要： A speech recognition system utilizes both split matrix and split vector quantizers as front ends to a second stage speech classifier such as hidden Markov models (HMMs) to, for example, efficiently utilize processing resources and improve speech recognition performance. Fuzzy split matrix quantization (FSMQ) exploits the "evolution" of the speech short-term spectral envelopes as well as frequency domain information, and fuzzy split vector quantization (FSVQ) primarily operates on frequency domain information. Time domain information may be substantially limited which may introduce error into the matrix quantization, and the FSVQ may provide error compensation. Additionally, acoustic noise influence may affect particular frequency domain subbands. This system also, for example, exploits the localized noise by efficiently allocating enhanced processing technology to target noise-affected input signal parameters and minimize noise influence. The enhanced processing technology includes a weighted LSP and signal energy related distance measure in training Linde-Buzo-Gray (LBG) algorithm and during recognition. Multiple codebooks may also be combined to form single respective codebooks for split matrix and split vector quantization to lower processing resources demand.

摘要翻译： 语音识别系统利用分割矩阵和分割矢量量化器作为第二级语音分类器的前端，例如隐马尔可夫模型（HMM），以例如有效利用处理资源并改善语音识别性能。 模糊分割矩阵量化（FSMQ）利用语音短期频谱包络的​​“演化”以及频域信息，模糊分割矢量量化（FSVQ）主要对频域信息进行操作。 时域信息可能基本上受到限制，这可能会将错误引入到矩阵量化中，并且FSVQ可以提供误差补偿。 此外，声学噪声的影响可能会影响特定的频域子频带。 例如，该系统还通过有效地分配增强的处理技术来目标受噪声影响的输入信号参数并最小化噪声影响来利用局部噪声。 增强处理技术包括训练林德 - 布佐 - 格雷（LBG）算法和识别期间的加权LSP和信号能量相关距离测量。 多个码本也可以组合以形成用于分割矩阵和分割矢量量化的单个相应码本，以降低处理资源需求。

公开(公告)号：US06219642B1

公开(公告)日：2001-04-17

申请号：US09166648

申请日：1998-10-05

申请人： Safdar M. Asghar ,  Lin Cong

发明人： Safdar M. Asghar ,  Lin Cong

IPC分类号： G10L1514

CPC分类号： G10L15/20 ,  G10L15/02 ,  G10L15/144

摘要： A speech recognition system utilizes multiple quantizers to process frequency parameters and mean compensated frequency parameters derived from an input signal. The quantizers may be matrix and vector quantizer pairs, and such quantizer pairs may also function as front ends to a second stage speech classifiers such as hidden Markov models (HMMs) and/or utilizes neural network postprocessing to, for example, improve speech recognition performance. Mean compensating the frequency parameters can remove noise frequency components that remain approximately constant during the duration of the input signal. HMM initial state and state transition probabilities derived from common quantizer types and the same input signal may be consolidated to improve recognition system performance and efficiency. Matrix quantization exploits the “evolution” of the speech short-term spectral envelopes as well as frequency domain information, and vector quantization (VQ) primarily operates on frequency domain information. Time domain information may be substantially limited which may introduce error into the matrix quantization, and the VQ may provide error compensation. The matrix and vector quantizers may split spectral subbands to target selected frequencies for enhanced processing and may use fuzzy associations to develop fuzzy observation sequence data. A mixer may provide a variety of input data to the neural network for classification determination. Fuzzy operators may be utilized to reduce quantization error. Multiple codebooks may also be combined to form single respective codebooks for split matrix and split vector quantization to reduce processing resources demand.

摘要翻译： 语音识别系统利用多个量化器来处理来自输入信号的频率参数和平均补偿频率参数。 量化器可以是矩阵和矢量量化器对，并且这样的量化器对还可以用作第二阶段语音分类器（例如隐马尔可夫模型（HMM））的前端，和/或利用神经网络后处理来例如改善语音识别性能 。 平均补偿频率参数可以消除在输入信号的持续时间内保持近似恒定的噪声频率分量。 可以整合从公共量化器类型和相同输入信号导出的HMM初始状态和状态转移概率，以提高识别系统的性能和效率。 矩阵量化利用语音短期频谱包络和频域信息的“演化”，矢量量化（VQ）主要对频域信息进行操作。 时域信息可能基本上受到限制，这可能会将错误引入到矩阵量化中，并且VQ可以提供误差补偿。 矩阵和矢量量化器可以将频谱子带分解成目标选择的频率用于增强处理，并且可以使用模糊关联来开发模糊观测序列数据。 混合器可以向神经网络提供各种输入数据以进行分类确定。 可以使用模糊算子来减少量化误差。 多个码本也可以组合以形成用于分割矩阵和分割矢量量化的单个相应码本，以减少处理资源需求。

公开(公告)号：US6003003A

公开(公告)日：1999-12-14

申请号：US883979

申请日：1997-06-27

申请人： Safdar M. Asghar ,  Lin Cong

发明人： Safdar M. Asghar ,  Lin Cong

IPC分类号： G10L15/02 ,  G10L15/06 ,  G10L7/08

CPC分类号： G10L15/02 ,  G10L2015/0631

摘要： In one embodiment, a speech recognition system is organized with a fuzzy matrix quantizer with a single codebook representing u codewords. The single codebook is designed with entries from u codebooks which are designed with respective words at multiple signal to noise ratio levels. Such entries are, in one embodiment, centroids of clustered training data. The training data is, in one embodiment, derived from line spectral frequency pairs representing respective speech input signals at various signal to noise ratios. The single codebook trained in this manner provides a codebook for a robust front end speech processor, such as the fuzzy matrix quantizer, for training a speech classifier such as a u hidden Markov models and a speech post classifier such as a neural network. In one embodiment, a fuzzy Viterbi algorithm is used with the hidden Markov models to describe the speech input signal probabilistically.

摘要翻译： 在一个实施例中，语音识别系统由具有代表u码字的单个码本的模糊矩阵量化器组织。 单码本被设计为来自u码本的条目，其被设计为具有多个信噪比水平的相应字。 在一个实施例中，这样的条目是聚类训练数据的质心。 在一个实施例中，训练数据来自于以各种信噪比表示各个语音输入信号的线谱频率对。 以这种方式训练的单个码本提供用于训练语音分类器（诸如，隐藏的马尔可夫模型）和诸如神经网络的语音后分类器之类的鲁棒前端语音处理器（例如模糊矩阵量化器）的码本。 在一个实施例中，使用模糊维特比算法与隐马尔可夫模型概率地描述语音输入信号。

公开(公告)号：US06418412B1

公开(公告)日：2002-07-09

申请号：US09649737

申请日：2000-08-28

申请人： Safdar M. Asghar ,  Lin Cong

发明人： Safdar M. Asghar ,  Lin Cong

IPC分类号： G10L1514

CPC分类号： G10L15/20 ,  G10L15/02 ,  G10L15/144

摘要： A speech recognition system utilizes multiple quantizers to process frequency parameters and mean compensated frequency parameters derived from an input signal. The quantizers may be matrix and vector quantizer pairs, and such quantizer pairs may also function as front ends to a second stage speech classifiers such as hidden Markov models (HMMs) and/or utilizes neural network postprocessing to, for example, improve speech recognition performance. Mean compensating the frequency parameters can remove noise frequency components that remain approximately constant during the duration of the input signal. HMM initial state and state transition probabilities derived from common quantizer types and the same input signal may be consolidated to improve recognition system performance and efficiency. Matrix quantization exploits the “evolution” of the speech short-term spectral envelopes as well as frequency domain information, and vector quantization (VQ) primarily operates on frequency domain information. Time domain information may be substantially limited which may introduce error into the matrix quantization, and the VQ may provide error compensation. The matrix and vector quantizers may split spectral subbands to target selected frequencies for enhanced processing and may use fuzzy associations to develop fuzzy observation sequence data. A mixer may provide a variety of input data to the neural network for classification determination. Fuzzy operators may be utilized to reduce quantization error. Multiple codebooks may also be combined to form single respective codebooks for split matrix and split vector quantization to reduce processing resources demand.

摘要翻译： 语音识别系统利用多个量化器来处理来自输入信号的频率参数和平均补偿频率参数。 量化器可以是矩阵和矢量量化器对，并且这样的量化器对还可以用作第二阶段语音分类器（例如隐马尔可夫模型（HMM））的前端，和/或利用神经网络后处理来例如改善语音识别性能 。 平均补偿频率参数可以消除在输入信号的持续时间内保持近似恒定的噪声频率分量。 可以整合从公共量化器类型和相同输入信号导出的HMM初始状态和状态转移概率，以提高识别系统的性能和效率。 矩阵量化利用语音短期频谱包络和频域信息的“演化”，矢量量化（VQ）主要对频域信息进行操作。 时域信息可能基本上受到限制，这可能会将错误引入到矩阵量化中，并且VQ可以提供误差补偿。 矩阵和矢量量化器可以将频谱子带分解成目标选择的频率用于增强处理，并且可以使用模糊关联来开发模糊观测序列数据。 混合器可以向神经网络提供各种输入数据以进行分类确定。 可以使用模糊算子来减少量化误差。 多个码本也可以组合以形成用于分割矩阵和分割矢量量化的单个相应码本，以减少处理资源需求。

公开(公告)号：US06347297B1

公开(公告)日：2002-02-12

申请号：US09166640

申请日：1998-10-05

申请人： Safdar M. Asghar ,  Lin Cong

发明人： Safdar M. Asghar ,  Lin Cong

IPC分类号： G10L1508

CPC分类号： G10L15/02 ,  G10L15/144

摘要： A speech recognition system utilizes both matrix and vector quantizers as front ends to a second stage speech classifier such as hidden Markov models (HMMs) and utilizes neural network postprocessing to, for example, improve speech recognition performance. Matrix quantization exploits the “evolution” of the speech short-term spectral envelopes as well as frequency domain information, and vector quantization (VQ) primarily operates on frequency domain information. Time domain information may be substantially limited which may introduce error into the matrix quantization, and the VQ may provide error compensation. The matrix and vector quantizers may split spectral subbands to target selected frequencies for enhanced processing and may use fuzzy associations to develop fuzzy observation sequence data. A mixer provides a variety of input data to the neural network for classification determination. The neural network's ability to analyze the input data generally enhances recognition accuracy. Fuzzy operators may be utilized to reduce quantization error. Multiple codebooks may also be combined to form single respective codebooks for split matrix and split vector quantization to reduce processing resources demand.

摘要翻译： 语音识别系统利用矩阵和矢量量化器作为第二级语音分类器的前端，例如隐马尔可夫模型（HMM），并利用神经网络后处理来改善语音识别性能。 矩阵量化利用语音短期频谱包络和频域信息的“演化”，矢量量化（VQ）主要对频域信息进行操作。 时域信息可能基本上受到限制，这可能会将错误引入到矩阵量化中，并且VQ可以提供误差补偿。 矩阵和矢量量化器可以将频谱子带分解成目标选择的频率用于增强处理，并且可以使用模糊关联来开发模糊观测序列数据。 混合器为神经网络提供各种输入数据，用于分类确定。 神经网络分析输入数据的能力通常提高了识别精度。 可以使用模糊算子来减少量化误差。 多个码本也可以组合以形成用于分割矩阵和分割矢量量化的单个相应码本，以减少处理资源需求。

公开(公告)号：US6070136A

公开(公告)日：2000-05-30

申请号：US957902

申请日：1997-10-27

申请人： Lin Cong ,  Safdar M. Asghar

发明人： Lin Cong ,  Safdar M. Asghar

IPC分类号： H04B1/66 ,  G10L1/00

CPC分类号： G10L15/32 ,  G10L15/142

摘要： A speech recognition system utilizes both matrix and vector quantizers as front ends to a second stage speech classifier. Matrix quantization exploits input signal information in both frequency and time domains, and the vector quantizer primarily operates on frequency domain information. However, in some circumstances, time domain information may be substantially limited which may introduce error into the matrix quantization. Information derived from vector quantization may be utilized by a hybrid decision generator to error compensate information derived from matrix quantization. Additionally, fuzz methods of quantization and robust distance measures may be introduced to also enhance speech recognition accuracy. Furthermore, other speech classification stages may be used, such as hidden Markov models which introduce probabilistic processes to further enhance speech recognition accuracy. Multiple codebooks may also be combined to form single respective codebooks for matrix and vector quantization to lessen the demand on processing resources.

摘要翻译： 语音识别系统利用矩阵和矢量量化器作为第二级语音分类器的前端。 矩阵量化利用频域和时域中的输入信号信息，矢量量化器主要对频域信息进行操作。 然而，在某些情况下，时域信息可能被大体上限制，这可能将错误引入到矩阵量化中。 从矢量量化得到的信息可以由混合决策发生器利用来对从矩阵量化导出的信息进行误差补偿。 此外，可以引入量化和鲁棒距离度量的模糊方法以增强语音识别精度。 此外，可以使用其他语音分类阶段，例如引入概率过程以进一步增强语音识别精度的隐马尔可夫模型。 多个码本也可以组合以形成用于矩阵和矢量量化的单个相应码本，以减少对处理资源的需求。

公开(公告)号：US06044343A

公开(公告)日：2000-03-28

申请号：US883978

申请日：1997-06-27

申请人： Lin Cong ,  Safdar M. Asghar

发明人： Lin Cong ,  Safdar M. Asghar

IPC分类号： G10L15/06 ,  G10L15/20

CPC分类号： G10L15/063 ,  G10L15/20

摘要： One embodiment of a speech recognition system is organized with speech input signal preprocessing and feature extraction followed by a fuzzy matrix quantizer (FMQ) designed with respective codebook sets at multiple signal to noise ratios. The FMQ quantizes various training words from a set of vocabulary words and produces observation sequences O output data to train a hidden Markov model (HMM) processes .lambda.j and produces fuzzy distance measure output data for each vocabulary word codebook. A fuzzy Viterbi algorithm is used by a processor to compute maximum likelihood probabilities PR(O.vertline..lambda.j) for each vocabulary word. The fuzzy distance measures and maximum likelihood probabilities are mixed in a variety of ways to preferably optimize speech recognition accuracy and speech recognition speed performance.

摘要翻译： 语音识别系统的一个实施例用语音输入信号预处理和特征提取来组织，随后是以多个信噪比设置有相应码本集合的模糊矩阵量化器（FMQ）。 FMQ量化来自一组词汇单词的各种训练词，并产生观察序列O输出数据以训练隐马尔可夫模型（HMM）过程λj，并为每个词汇词码本生成模糊距离测量输出数据。 处理器使用模糊维特比算法来计算每个词汇词的最大似然概率PR（O |λj）。 模糊距离测度和最大似然概率以各种方式混合，以优化语音识别精度和语音识别速度性能。

公开(公告)号：US6032116A

公开(公告)日：2000-02-29

申请号：US883980

申请日：1997-06-27

申请人： Safdar M. Asghar ,  Lin Cong

发明人： Safdar M. Asghar ,  Lin Cong

IPC分类号： G10L15/02 ,  G10L15/10 ,  G10L15/20 ,  G10L7/08

CPC分类号： G10L15/20 ,  G10L15/02 ,  G10L15/10

摘要： One embodiment of a speech recognition system is organized with speech input signal preprocessing and feature extraction followed by a fuzzy matrix quantizer (FMQ). Frames of the speech input signal are represented by a vector .function. of line spectral pair frequencies and are fuzzy matrix quantized to respective a vector .function. entries in a codebook of the FMQ. A distance measure between .function. and .function., d(.function.,.function.), is defined as ##EQU1## where the constants .alpha..sub.1, a.sub.2, .beta..sub.1 and .beta..sub.2 are set to substantially minimize quantization error, and e.sub.i is the error power spectrum of the speech input signal and a predicted speech input signal at the ith line spectral pair frequency of the speech input signal. The speech recognition system may also include hidden Markov models and neural networks, such as a multilevel perceptron neural network, speech classifiers.

摘要翻译： 用语音输入信号预处理和特征提取后跟模糊矩阵量化器（FMQ）来组织语音识别系统的一个实施例。 语音输入信号的帧由线谱对频率的向量f表示，并且是模糊矩阵量化到FMQ的码本中的矢量+ E，cir f + EE条目。 定义f和+ E之间的距离度量，cir f + EE，d（f，+ E，cir f + EE），其中常数α1，α2，β1和β2被设置为基本上最小化量化误差 ，ei是语音输入信号的误差功率谱和语音输入信号的第i线频谱对频率处的预测语音输入信号。 语音识别系统还可以包括隐马尔可夫模型和神经网络，例如多层感知器神经网络，语音分类器。

公开(公告)号：US6009391A

公开(公告)日：1999-12-28

申请号：US907145

申请日：1997-08-06

申请人： Safdar M. Asghar ,  Lin Cong

发明人： Safdar M. Asghar ,  Lin Cong

IPC分类号： G10L15/02 ,  G10L15/10 ,  G10L15/20 ,  G01L5/06

CPC分类号： G10L15/20 ,  G10L15/02 ,  G10L15/10

摘要： One embodiment of a speech recognition system is organized with speech input signal preprocessing and feature extraction followed by a fuzzy matrix quantizer (FMQ). Frames of the speech input signal are represented in a matrix by a vectorf of line spectral pair frequencies and energy coefficients and are fuzzy matrix quantized to respective vector f entries of a matrix codeword in a codebook of the FMQ. The energy coefficients include the original energy and the first and second derivatives of the original energy which increase recognition accuracy by, for example, being generally distinctive speech input signal parameters and providing noise signal suppression especially when the noise signal has a relatively constant energy over at least two time frame intervals. To reduce data while maintaining sufficient resolution, the energy coefficients may be normalized and logarithmically represented. A distance measure between f and f, d(f, f), is defined as ##EQU1## where the constants .alpha..sub.1, .alpha..sub.2, .beta..sub.1 and .beta..sub.2 are set to substantially minimize quantization error, e.sub.i is the error power spectrum of the speech input signal and a predicted speech input signal at the ith line spectral pair frequency of the speech input signal, the first G LSP frequencies are most likely to be frequency shifted by noise, and the last P+3 coefficients represent the three energy coefficients. This robust distance measure can be used to enhance speech recognition performance in generally any speech recognition system using line spectral pair based distance measures.

摘要翻译： 用语音输入信号预处理和特征提取后跟模糊矩阵量化器（FMQ）来组织语音识别系统的一个实施例。 语音输入信号的帧通过线谱对频率和能量系数的矢量以矩阵表示，并且是模糊矩阵量化到FMQ的码本中的矩阵码字的相应向量+ E，cir f + EE条目。 能量系数包括原始能量和原始能量的第一和第二导数，其通过例如通常是有区别的语音输入信号参数来提高识别精度，并且提供噪声信号抑制，特别是当噪声信号具有相对恒定的能量时 至少两个时间间隔。 为了在保持足够的分辨率的同时减少数据，可以对能量系数进行归一化和对数表示。 定义f和+ E之间的距离度量，cir f + EE，d（f，+ E，cir f + EE），其中常数α1，α2，β1和β2被设置为基本上最小化量化 误差，ei是语音输入信号的误差功率谱和语音输入信号的第i线频谱对频率处的预测语音输入信号，第一G LSP频率最有可能被噪声频移，最后 P + 3系数表示三个能量系数。 这种可靠的距离测量可以用于在通常使用基于线光谱对的距离测量的任何语音识别系统中增强语音识别性能。

公开(公告)号：US20050022888A1

公开(公告)日：2005-02-03

申请号：US10631488

申请日：2003-07-31

申请人： Victor Samper ,  Lin Cong ,  Hongmiao Ji

发明人： Victor Samper ,  Lin Cong ,  Hongmiao Ji

IPC分类号： B01L3/00 ,  B81C3/00 ,  F15C1/04

CPC分类号： B81C3/001 ,  B01L3/502707 ,  B01L2200/0689 ,  B01L2200/12 ,  B01L2200/141 ,  B01L2300/0887 ,  B01L2300/1827 ,  B81B2201/058 ,  Y10T137/2196 ,  Y10T156/10

摘要： Microfluidics chips and methods of use are described, comprising a pair of wafers, at least one having a patterned surface, and two polymeric barrier films between the wafers conforming to the patterned surface. The polymeric barrier films allow the wafers of the inventive microfluidics chips to be reused without cleaning.

摘要翻译： 描述了微流体芯片和使用方法，包括一对晶片，至少一个具有图案化表面的晶片，以及在与图案化表面一致的晶片之间的两个聚合物阻挡膜。 聚合物阻挡膜允许本发明的微流体芯片的晶片在不进行清洁的情况下重复使用。