1. A speaker independent isolated word recognition apparatus, comprising: digitizing means for digitizing a speech signal and subjecting the digitized speech signal to spectral analysis at constant temporal intervals using fast Fourier transform, to obtain an analysis result; means connected to said digitizing means for subjecting the analysis result to an orthogonal transformation to obtain cepstral parameters and a logarithm of a total energy contained in each temporal interval to yield characteristic parameters of the speech signal for each temporal interval; means for detecting word ends through an energy level of the respective speech signal; and a recognizer (RNA), in which complete words are modelled with Markov model automata of a left-to-right type with recursion on states, each of which corresponds to an acoustic portion of the word, and in which the recognition is carried out through a dynamic programming according to a Viterbi algorithm on all automata for finding one with a minimum cost path, which corresponds to the recognized word indicated at output (PR), emission possibilities being calculate with a neural network with feedback having parallel processing neurons, the neural network being trained by: initialization: a. initialization of the neural network with small random synaptic weights; b. creation of a first segmentation by segmenting a training of set words uniformly; iteration by: initialization of the training set with all the segmented words; random choice of a word not already learned; updating of synaptic weights w.sub.ij for a word by applying a correlative training by varying a neural network input according to a window sliding from left to right on the word and supplying for every input window a suitable objective vector at an output, constructed by setting a 1 on the neuron corresponding to a state to which the input window belongs, according to the segmentation, and by setting 0 on all the other neurons; segmentation recomputation for the considered word, by using the neural network as previously trained, and performing a dynamic programming only with correct model; updating of the segmentation S.sub.t+1 ; if there still are non considered words in the training set, repeat the random choice; recomputation of transition probabilities of automata; and if the number of iterations on the training set is greater than a maximum preset number NMAX, terminate or return to initialization of the training set.