Abstract: A navigation apparatus and navigation method for an automobile in which a map is visually displayed and a desired destination can be set by speaking the name of such destination. A voice recognition section recognizes the destination and marks it on the map that is being displayed and the best route to the displayed destination is then shown on the map to be followed by the driver of the automobile.

Abstract: Voice processing for recognizing a predetermined voice such as a place name is performed by a voice processing section 14 from an audio signal inputted from a microphone 11 on the basis of an operation of a talk switch 18. When a map display is based on the recognized place name is performed, an incorrect reading and a place name commonly mistaken can be also recognized. Accordingly, a high grade operation of a navigation apparatus can be simply performed without obstructing an operator driving while a car.

Abstract: A book database stores at least phonetic signal information including phoneme information and rhythm information as document data, a central system transmits phonetic signal information stored on the book database to a terminal and the terminal receives the phonetic signal information is then carried out at the terminal and the document is then recited via synthesized sounds.

Abstract: A mapping determination method for obtaining mapping F from an N-dimensional metric vector space .OMEGA..sub.N to an M-dimensional metric vector space .OMEGA..sub.M has the following steps to get the optimal mapping quickly and positively. In the first step, complete, periodic, L.sub.m basic functions g.sub.m (X) according to the distribution of samples classified into Q categories on the N-dimensional metric vector space .OMEGA..sub.N are set. In the second step, a function f.sub.m (X) indicating the m-th component of the mapping F is expressed with the linear sum of the functions g.sub.m (X) and L.sub.m coefficients c.sub.m. The third step provides Q teacher vectors T.sub.q =(t.sub.q.1, t.sub.q.2, t.sub.q.3, . . . , t.sub.q.M) (where q=1, 2, . . . , Q) for the categories on the M-dimensional metric vector space .OMEGA..sub.M, calculates the specified estimation function J, and obtains the coefficients c.sub.m which minimize the estimation function J. In the fourth step, the coefficients c.sub.

Abstract: A voice recognizing method in which a plurality of voice recognition objective words are provided. Scores are accumulated for an unknown input voice signal as compared to the voice recognition objective words by using parameters which are calculated in advance. Upon receipt of an unknown voice signal, a corresponding voice recognition objective word is extracted and recognized. The voice recognition objective words are structured into an overlapping hierarchical structure by using correlation values between each pair of voice recognition objective words. This correlation may be computed from acoustic features, HMM parameters or the like. Score calculation is performed on the unknown input voice signal by using a dictionary of the voice recognition objective words structured in the hierarchical structure. Upon preliminary recognition, the dictionary of the voice recognition objective words is resorted without recalculation of the correlation values.

Abstract: A voice recognition device according to the present invention including a voice analyzer for acoustically analyzing voice every predetermined frame unit to extract a feature vector X, a converter for subjecting the feature vector X output from the analyzer to a predetermined conversion process, and a voice recognizer for recognizing the voice on the basis of a new feature vector output from the converter, wherein the converter conducts the predetermined conversion processing according to a mapping F from an N-dimensional vector space .OMEGA..sub.N to an M-dimensional vector space .OMEGA..sub.M, the feature vector X is a vector on the N-dimensional vector space .OMEGA..sub.N and the function f.sub.m (X) of an m-th component of the mapping F is represented by the following linear summation of the products of functions g.sub.m.sup.k (X) and coefficients c.sub.m.sup.k of L.sub.m : ##EQU1## Each function g.sub.m.sup.k (X) may be set to a monomial.

Abstract: A map determination method and apparatus for calculating the coefficients to give a minimum evaluation function quickly and reliably where a map is expressed as the linear sum of a function g.sub.i (X) and a coefficient c.sub.i while a map for transforming a N-dimensional vector (x.sub.0, x.sub.1, x.sub.2, x.sub.3) to a M-dimensional vector y is being decided. The coefficient ci for the map is obtained by giving a learning sample and a teaching sample, obtaining an evaluation function and solving a simultaneous linear equation for which the partial differential is zero.