Matsushita Electric Industrial Co., Ltd.

Panasonic Corporation

Eiichi Tsuboka

Fumio Daio

Hideo Kaiya

Hirokazu Yoshino

Takeji Kimura

Junichi Nakahashi

Masao Nakazawa

Tatsuo Fujita

Haruyasu Yamada

Teruo Kitani

Takaaki Baba

Shuzi Harada

Tomio Oyama

Tatuo Fujita

An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Authentication system

Speech recognition apparatus

A codebook for vector quantization is speaker-adapted or a speaker is normalized so that it complies with the codebook using a small number of samples for learning. A deviation vector is set for the centroid of each cluster or an input vector. The deviation vector is set so that a separately defined objective function is maximized if it is defined to be maximized, or minimized if it is defined to be minimized. The maximization or minimization is performed using samples for learning obtained from a speaker who uses the system when the centroid or input vector is moved by an amount corresponding to the deviation vector. By moving the centroid or input vector using the deviation vector, speaker adaptation is performed if the former is moved and speaker normalization is performed if the latter is moved.

Vector quantizer

A method and apparatus for estimating parameters of a new hidden Markov model (HMM) applicable to pattern recognition such as recognition of speech signals which are time series signals, and a method and apparatus for pattern recognition employing this HMM are provided. It is easily applicable to speech and other time series signals. In particular, the pattern recognition degree of a time series observation vector signaly, received from an information source, is calculated by using function values u(y,1), u(y,2), . . . , u(y,M) and occurrence probabilities of signals C.sub.1, C.sub.2, . . . , C.sub.M which are composed of set C, where u(y,m) is the image into which the pair (C.sub.m,Y) is mapped, u(y,m).di-elect cons.U(U=[a,b], a,b.di-elect cons.R.sup.1 and 0.ltoreq.a.ltoreq.b) and y.di-elect cons.R.sup.n (R.sup.n : n-dimensional Euclidean space). C is a set of signals against which the observation vector signal y is compared.

Method and apparatus for pattern recognition employing the Hidden Markov

A method and apparatus for estimating parameters of a new hidden Markov model (HMM) applicable to pattern recognition such as recognition of speech signals which are time series signals, and a method and apparatus for pattern recognition employing this HMM. It is easily applicable to speech and other time series signals. In particular, the pattern recognition degree of a time series observation vector signal y, received from an information source, is calculated by using function values u(y,1), u(y,2), . . . , u(y,M) and occurrence probabilities of signals C.sub.1, C.sub.2, . . . , C.sub.M which are composed of set C, where u(y,m) is the image into which the pair (C.sub.m,Y) is mapped, u(y,m).epsilon.U (U=[a,b], a,b.epsilon.R.sup.1 and 0.ltoreq.a.ltoreq.b) and y.epsilon.R.sup.n (R.sup.n :n-dimensional Euclidean space). C is a set of signals against which the observation vector signal y is compared.

Method and apparatus for pattern recognition employing the hidden Markov

A method and apparatus are disclosed for estimating parameters of a new hidden Markov model (HMM) applicable to pattern recognition such as recognition of speech signals which are time series signals, and a method and apparatus for pattern recognition employing this HMM. It is easily applicable to speech and other time series signals. In particular, the pattern recognition degree of a time series observation vector signal y, received from an information source, is calculated by using function values u(y,1), u(y,2), . . . , u(y,M) and occurrence probabilities of signals C.sub.1, C.sub.2, . . . , C.sub.M which are composed of set C, where u(y,m) is the image into which the pair (C.sub.m, Y) is mapped, u(y,m).di-elect cons.U (U=[a,b], a,b.di-elect cons.R.sup.1 and 0.ltoreq.a.ltoreq.b) and y.di-elect cons.R.sup.n (R.sup.n : n-dimensional Euclidean space). C is a set of signals against which the observation vector signal y is compared.

Method and apparatus for pattern recognition employing the hidden markov

A neural network with a high recognition rate when applied to static patterns is made applicable to dynamic time series patterns such as voice signals. Plural units with one or more inputs and outputs are interconnected, and a unique load coefficient is assigned to each connection to weight the signals flowing through that connection. The neural network includes an input unit group to which are input the components of plural vectors included in the input feature vector series {y(t)}; an output unit which outputs the converted vectors, which are produced by passing the input vectors through each unit and the associated connections; and J paths from input unit group to the output unit group. The units are connected to form a Hidden Markov Model wherein each signal path identified as j=1, 2, . . . , J corresponds to the same state.

Time series signal analyzer including neural network having path groups

An analyzing system analyzes object signals, particularly voice signals, by estimating a generation likelihood of an observation vector sequence being a time series of feature vectors with use of a Markov model having a plurality of states and given transition probabilities from state to state. A state designation section determines a state i at a time t stochastically using the Markov model. Plural predictors, each of which is composed of a neural network and is provided per each state of the Markov model, are provided for generating a predictional vector of the feature vector x.sub.t in the state i at the time t based on values of the feature vectors other than the feature vector x.sub.t. A first calculation section calculates an error vector of the predictional vector to the feature vector x.sub.T. A second calculation section calculates a generation likelihood of the error vector using a predetermined probability distribution of the error vector according to which the error vector is generated.

Voice analyzing system using hidden Markov model and having plural

A pattern recognition apparatus using the hidden Markov model technique in which parameters for defining a mean vector representing the probability density function in each one of plural states for composing the hidden Markov model vary with time. Accordingly, the recognition precision may be enhanced when this apparatus is used in, for example, voice recognition.

Pattern recognition apparatus

Devices for performing fast Hadamard transforms based upon a predetermined algorithm are provided. A buffer memory (random-access memory) with the capacity equal to the number of components of an input vector to be transformed is provided and is addressed in such a way computation of Hadamard transforms can be performed at faster speeds with a higher degree of efficiency.

Fast hadamard transform device

An image display apparatus has an: image signal receiver for simultaneously receiving two different image signals, a first image signal and a second image signal; a memory for writing therein the second image signal; controlling means for controlling the writing position, writing frequency, writing sequence, reading-out sequence, reading-out frequency and start-timing of reading-out of the second image signal; a display for displaying full picture information or partial picture information of the second image signal a part of a displayed picture of the first image signal by selectively reading-out the second image signal under the control of the controller; and zoom-up device for stepwisely or continuously varying the writing position and reading-out position of the second image signal controlled by the controller so as to zoomwisely display the full or partial picture of the second image signal on the picture of the first image signal.

Composite image display apparatus

A television receiver is disclosed, which receives two television signals and compresses a time axis of one of the television signals and inserts the compressed signal to a portion of the other television signal to display both signals on the same screen. A signal indicating a boundary of the two images is produced from a synchronizing signal derived from one of the two received television signals and the boundary indicating signal is superimposed on one or both of a brightness signal and a chrominance signal to display the boundary.

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Neyagawa, Japan

Eiichi Tsuboka