Disclosed is an image processing apparatus for processing an input image and recording the image on a recording medium, and particularly a color copying apparatus which permits color copying of an original, and makes it possible to discriminate a specific original for the sake of prevention of forgery of a banknote, a negotiable instrument, or the like. The specific original is provided with copying processing which is different from that provided for an ordinary original.

Image processing apparatus

An image processing apparatus for processing an input image and recording the image on a recording medium, and particularly a color copying apparatus which permits color copying of an original, and makes it possible to discriminate a specific original for the sake of prevention of forgery of a banknote, a negotiable instrument, or the like. The specific original is provided with copying processing which is different from that provided for an ordinary original.

Apparatus with anti-forgery provision

A speech recognition apparatus includes a speech signal analyzing circuit for time-sequentially generating acoustic parameter patterns representing the phonetic features of speech signals, and phoneme reference memories each storing a plurality of reference parameter pattern vectors. A phoneme pattern vector from the speech signal analyzing circuit is compared with each of the reference pattern vectors stored in the phoneme reference memories in order to recognize an input speech. The speech signal analyzing circuit has a parameter extraction circuit for time-sequentially extracting acoustic parameter patterns representing the speech signal, a first phoneme pattern vector memory for storing a phoneme pattern vector including an acoustic parameter pattern of each frame from the parameter extraction circuit, and a second phoneme pattern vector memory for storing a phoneme pattern vector including a plurality of parameter patterns from the parameter extraction circuit.

Speech recognition apparatus

Speaker-independent word recognition is performed, based on a small acoustically distinct vocabulary, with minimal hardware requirements. After a simple preconditioning filter, the zero crossing intervals of the input speech are measured and sorted by duration, to provide a rough measure of the frequency distribution within each input frame. The distribution of zero crossing intervals is transformed into a binary feature vector, which is compared with each reference template using a modified Hamming distance measure. A dynamic time warping algorithm is used to permit recognition of various speaker rates, and to economize on the reference template storage requirements. A mask vector with each reference vector on a template is used to ignore insignificant (or speaker-dependent) features of the words detected.

Speaker-independent word recognizer

A continuous speech recognition system determines the similarity between input patterns and reference patterns over time such that similarities between previously spoken speech patterns and reference patterns are determined while speech continues to be spoken. Degrees of dissimilarity at arbitrary reference pattern word times are determined asymptotically and are recorded. The minimum degree of dissimilarity is determined and the corresponding word is categorized. Recognition decisions are ultimately made in reverse chronological order.

Continuous speech recognition system

A computer for calculating the similarity between first and second patterns, each represented by a sequence of feature vectors, comprises a calculating circuit for calculating a weighting factor from feature vectors in the first pattern, a weighting circuit for applying the weighting factor to the feature vectors of the second pattern to calculate an estimated vector for the second pattern, and a similarity calculating circuit for calculating and determining the similarity between the first and the second patterns on the basis of the feature vector of the first pattern and of the estimated vector of the second pattern.

Computer for calculating the similarity between patterns

PURPOSE:To ensure a highly efficient mathcing of patterns, by calculating the degree of analogy with use of the vector series of the input pattern presumed based on the relation among the vector series of standard pattern CONSTITUTION:For a calculation of (i+1) stage, the feature vectors ai and ai+1 of standard pattern A are read iut of the storage part 1 Then GAMMAi=ai+1/ai is obtained at the division part 11, and a multiplication is carried out through the multiplier 13 between the presumed vectors b' and GAMMAi obtained at the stage (i) to obtain the vector (b) The vector bi of the input pattern B is read out of the input pattern storage part 2 The vectors bi and b are multiplied by the weight coefficient each by the multipliers 10 and 15, and the both vectors are added together through the adder 17 to obtain the presumed vector b' at the (i+1) stage The distance between vectors ai+1 and b' is calculated at the distance calculating part 20, and the degree of analogy is calculated at the analogy degree calculating part 21 for the (i+1) stage Thus the fluctuation component of the feature vector can be reduced to ensure a highly efficient matching of patterns

Atsuo Tanaka

Papers

Computer for calculating the similarity between patterns

Calculating system for pattern analogy degree