DEVELOPMENT AND RESEARCH INTO NEURONET ALGORITHM OF SPEAKER-INDEPENDENT SPEECH RECOGNITION
At present the problem solving of the accuracy increase of speaker-independent Russian speech recognition is urgent. The article suggests the problem solving variant using neural networks and analyzing the dependence of the accuracy of speaker-independent speech recognition by investigated neuronet algorithms from teaching speakers quantity. As speech signal parameters that the recognition was conducted by, the logarithm of the energy of mel-frequency cepstral coefficients was chosen. The models of basic (multilayer perceptron) and collective (bagging-ensemble) of neuronet algorithms of speaker-independent Russian speech recognition were built. On the material of the intrinsic speech corpus the experiments showed the advantage of collective neuronet recognition over a single neuronet recognizer and the dependence of the accuracy of speaker-independent speech recognition by investigated neuronet algorithms from teaching speakers quantity.