ОСОБЛИВОСТІ ВИКОРИСТАННЯ MEL-КЕПСТРАЛЬНИХ КОЕФІЦІЄНТІВ ПРИ РОЗПІЗНАВАННІ МОВЛЕННЯ

Abstract

One of the main forms of human interaction is speech. Nowadays, there are enough useful programs for speech recognition of people who have certain limitations. These programs translate the text that was spoken aloud into the text, to clearly understand what the person wants to say. The main purpose of speech recognition methods is to obtain information as an input voice signal for further clear translation. Modern methods of speech recognition use an important part - language modeling and acoustic modeling. Nowadays, there are quite a number of methods for translating a voice into a test. The best method of recognition is bas on hidden Markov models. Statistical models are using to display sequences of characters. Therefore, the method based on hidden Markov models is one of the best for solving such problems. The article investigates the methods of speech recognition of people with speech disorders in a short dictionary using mel-keptral coefficients. The proposed method applies the criterion used for unbiased estimation of the logarithmic spectrum to the spectral model represented by MEL coefficients. To solve the nonlinear minimization problem involved in the method, they provide an iterative algorithm whose convergence is guaranteed. Examples of speech analysis and results of an isolated word recognition experiment.