Abstract: The intended object of the present invention is to provide a more widely applicable means of diagnosing familial hyperlipemia with certainty. It has been found that this object can be attained by providing a method of detecting abnormality of lipid metabolism, wherein risk factors concerning abnormalities of lipid metabolism are detected on the basis of the correlation of 65 specific types of LDL receptor gene mutations with the abnormalities of lipid metabolism, as well as a method of detecting disease(s) by detecting risk factors for arteriosclerosis and/or ischemic heart diseases by employing as indices the abnormalities of lipid metabolism thus detected.

Abstract: Risk factors for diabetic onset in a subject are detected by using a gene coding for CD38 protein, which is originally identified as a human lymphocyte surface marker, as a risk gene, and detecting mutations in the gene.

Abstract: A voice synthesizing system can make necessary calculation amount satisfactorily small and can make necessary file size small. The system includes a compressed pitch segment database storing compressed voice waveform segments, a pitch developing portion reading out the voice waveform segment from the database and decompressing the compressed data for reproducing an original voice waveform segment when the voice waveform segment necessary for voice waveform synthesis is demanded, and a cache processing portion temporarily storing the voice waveform segment already used in voice waveform synthesis, and when voice waveform segment necessary for voice waveform synthesis is demanded, returning demanded voice waveform segment to a demander when demanded voice waveform segment is already stored, and obtaining the voice waveform segment from the database via the pitch developing portion to hold the obtained voice waveform segment and return to the demander when demanded voice waveform segment is not stored.

Abstract: Risk factors for diabetic onset in a subject are detected by using a gene coding for CD38 protein, which is originally identified as a human lymphocyte surface marker, as a risk gene, and detecting mutations in the gene.

Abstract: A reference pattern producing apparatus includes a data storage section, a learning coefficient storage section and a reference pattern producing section. The data storage section stores learning data of a content and a time series of features for each of a plurality of samples groups. The learning coefficient storage section stores learning coefficients to the plurality of sample groups. The reference pattern producing section repetitively learns the learning data using the learning coefficients to produce reference patterns.

Abstract: A method of recognizing a speaker of an input speech according to the distance between an input speech pattern, obtained by converting the input speech to a feature parameter series, and a reference pattern preliminarily registered as feature parameter series for each speaker is provided. Contents of the input and reference speech patterns is obtained by recognition. An identical section, in which the contents of the input and reference speech patterns are identical is determined. The distance between the input and reference speech patterns in the calculated identical content section is determined. The speaker of the input speech is recognized on the basis of the determined distance.

Abstract: A voice recognition system, method and storage medium is provided. The system includes a plurality of storage sections, a selection section, an adaptation section, a plurality of calculation sections, an adaptation section, a normalization section and a decision section. The method includes the steps for performing the functions associated with the sections.

Abstract: A speaker recognition device for judging whether or not an unknown speaker is an authentic registered speaker himself/herself executes `text verification using speaker independent speech recognition` and `speaker verification by comparison with a reference pattern of a password of a registered speaker`. A presentation section instructs the unknown speaker to input an ID and utter a specified text designated by a text generation section and a password. The `text verification` of the specified text is executed by a text verification section, and the `speaker verification` of the password is executed by a similarity calculation section. The judgment section judges that the unknown speaker is the authentic registered speaker himself/herself if both the results of the `text verification` and the `speaker verification` are affirmative.

Abstract: A joint circuit is provided to form a joint circuit without inserting terminals into a connector at a later stage. The joint circuit employs terminals 35 that have mount holes. The terminals 35 are connected in advance with ends of wires to be connected with each other among the wires which will form an automotive wiring harness. Partition walls 21 are provided in an insulation casing 20 to define a plurality of terminal connecting chambers 22, 23. The heads of bolts are embedded in the bottom walls of the respective terminal connecting chambers so that the shafts thereof project. The mount holes of the terminals at the ends of the wires to be connected with each other are fitted down on the bolt shafts and are tightened by nuts to form a joint circuit. This joint circuit includes a joint circuit for grounding.

Abstract: In a speaker recognition system, a tree-structured reference pattern storing unit has first through M-th node stages each of which has nodes that respectively store a reference pattern of inhibiting speakers. The reference pattern of each node of (N-1)-th node stage represents acoustic features in the reference patterns of predetermined ones of the nodes of the N-th node stage. An analysis unit analyzes input speech and converts the input speech into feature vectors. A similarities calculating unit calculates similarities between the feature vectors and the reference patterns of all of the inhibiting speakers. An inhibiting speaker selecting unit sorts the similarities and selects a predetermined number of inhibiting speakers.

Abstract: A speech recognition system, in which partial reference patterns, and cumulative similarities of these patterns, are stored in a temporary pattern memory. The partial reference patterns are to be used as subjects of a similarity computation with an input speech pattern that has its feature quantities extracted by a speech analyzing unit. A counting unit counts partial reference patterns having corresponding cumulative similarities that are higher than a threshold value stored in a threshold memory. A threshold computing unit computes a threshold of pruning from a correspondence relation between the number of partial reference patterns that have corresponding cumulative similarities that exceed the threshold, and the threshold. A similarity computing unit computes a similarity, with respect to the feature quantities, of partial reference patterns with corresponding cumulative similarities that are greater than the threshold of pruning.

Abstract: Speech input is converted into a feature vector series, which is held as an input pattern in an input pattern memory and which is also fed to a preliminary recognizer. A reference pattern memory stores reference patterns of recognition subject words. The preliminary recognizer executes preliminary recognition by calculating similarity measures between the input pattern and the reference patterns stored in a reference pattern memory, and obtains top N candidates using the calculated similarity measures. A reference pattern adapter executes adaptation of the reference patterns based on the reference patterns, the input pattern, the top N candidates, and newly stores the adapted reference patterns in the reference pattern memory. A final recognizer then executes speech recognition of the input pattern by using the newly-stored reference patterns corresponding to the top N candidates.

Abstract: A training unit selector takes out and compares phoneme data about recognition units stored in a recognition word dictionary and a training word dictionary, and selects a recognition unit from the recognition unit training words stored in the training word dictionary, the selected recognition unit resembling each selected recognition unit in a subject recognition word stored in the recognition word dictionary in phoneme context, the selected recognition unit being sent to a recognition unit reference pattern generator. The recognition unit reference pattern generator utilizes a recognition unit, which is best in accord in phoneme context among training data stored in a training data memory, concerning a recognition unit selected by the training unit selector.

Abstract: Speech data is converted into logarithmic spectrum data and orthogonally transformed to develop feature vectors. Normalization coefficient data and unit vector data are stored. An inner product of the feature vector data and the unit vector data is calculated. The inner product may be the average of inner products for a word or a sentence, or may be a regressive average of them. A normalization vector, which corresponds to a second or higher order curve obtained by least-square error approximation of the speech data on logarithmic spectrum space, is calculated on the transformed feature vector space by using the inner product, the normalization coefficient data, and the unit vector data. Normalization of the feature vectors is performed by subtracting the normalization vector from the feature vectors on the transformed feature vector space. Then, a recognition is performed based on the normalized feature vector.

Abstract: Each time a matching unit 22 finishes a matching at a time "i" between feature vectors of input speech which have been converted by an analyzer 1 and feature vectors of reference patterns stored in a reference pattern memory 2, and goes to a matching at a next time "i+1", a feature vector integrator 24 multiplies the feature vectors of the input speech or the reference patterns by a weight w1 in each of acoustic categories, successively integrates products, stores integrated values in respective frames of the reference patterns in feature vector accumulating buffers 26, and integrates and stores weights w1 in feature vector weight counters 27 corresponding to the respective acoustic categories.

Abstract: In a phoneme recognition apparatus, first distances between input vectors and reference vectors are determined. If the first distances are lower than a threshold value, the input vectors are identified as corresponding to the reference vectors. If the first distances are higher than the threshold value, the input vectors are identified as being indeterminate, and input differential vectors between the input vectors identified as corresponding to a reference vector and those identified as not corresponding to any of the reference vectors are determined. In addition, reference differential vectors between the reference vectors having corresponding input vectors and are those having no corresponding input vectors are determined. Second distances between the input differential vectors and the reference differential vectors are calculated and summed.

Abstract: A device for use in a speech recognizer or similar apparatus for normalizing the spectrum of speech as preprocessing for speech recognition. The device divides the spectrum of input speech at a predetermined frequency and determines a linear approximate line for each of the divided spectra such that the resulting approximate lines join each other at the point of division, thereby normalizing the spectrum.

Abstract: A speech recognizing device capable of recognizing a speech uttered in a noisy environment. The device uses low frequency data which are hardly susceptible to spectrum fluctuations for those portions of an input speech which are similar to vowels, while using entire frequency data for the other portions of the speech.