| Abstrakt: |
Within the framework of the traditional scope of investigations in the field of acoustic measurements, we consider an autoregressive model of the vocal tract, which is a key link in the speech apparatus of human beings. We mention the existence of an urgent problem of guaranteeing the stability of the autoregressive model in systems with adaptation of their parameters to the observed speech signals of short duration. To overcome this difficulty, we pose the problem of testing the stability of the autoregressive model and adjustment of its parameters according to the results of testing. The required investigations are based on the original authors' technique of the formant analysis of vowel sounds of speech via the synthesis of a recursive shaping filter in the mode of free oscillations. For the solution of the posed problem, we propose a procedure aimed at testing the stability of the autoregressive model of vocal tract and adjustment of its parameters. The method is based on a two-stage algorithm of transformation of the autoregressive model of vocal tract. In the first stage of transformation, the stability of the autoregressive model is checked according to the impulse response of the shaping filter. In the second stage, if the stability of the autoregressive model is violated, its impulse response is modified as a result of the element-by-element multiplication by a variable exponential quantity asymptotically convergent to zero. We develop a regular algorithm for recalculating a modified impulse response into the adjusted vector of autoregressive parameters in the second stage of transformation. According to the results of experimental verification of the proposed method, we make a conclusion that the guaranteed stability of the autoregressive model of the vocal tract is attained with minimal distortions in the frequency domain. The obtained results can be useful for the development and improvement of the systems of automatic speech recognition, digital speech communications, artificial intelligence, and other information systems based on the use of data compression and speech encoding according to the autoregressive model of the vocal tract in the course of automatic processing of speech signals. [ABSTRACT FROM AUTHOR] |
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