informИнформатикаInformatics1816-03012617-6963UIIP NASBinform-152Research ArticleОБРАБОТКА СИГНАЛОВ, ИЗОБРАЖЕНИЙ, РЕЧИ, ТЕКСТА И РАСПОЗНАВАНИЕ ОБРАЗОВSIGNAL, IMAGE, SPEECH, TEXT PROCESSING AND PATTERN RECOGNITIONАДАПТАЦИЯ СКРЫТЫХ МАРКОВСКИХ МОДЕЛЕЙ К РАСПОЗНАВАНИЮ ЭМОЦИОНАЛЬНО ОКРАШЕННОЙ РЕЧИADAPTIVE LEARNING OF HIDDEN MARKOV MODELS FOR EMOTIONAL SPEECHТкаченяА. В.TkacheniaA. V.tkachenia@gmail.com201406102016032127Copyright © Ткаченя А.В., 20162016Ткаченя А.В.Tkachenia A.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://inf.grid.by/jour/article/view/152

Рассматривается алгоритм интерактивной неконтролируемой оценки параметров скрытых марковских моделей (СММ). Решается задача адаптации СММ к эмоционально окрашенной речи. Для увеличения достоверности уточненных параметров СММ предлагается механизм забывания и обновления. Приводятся функциональная блок-схема рассматриваемого алгоритма адаптации СММ, а также полученные результаты улучшения эффективности распознавания эмоциональной речи.

An on-line unsupervised algorithm for estimating the hidden Markov models (HMM) parame-ters is presented. The problem of hidden Markov models adaptation to emotional speech is solved. To increase the reliability of estimated HMM parameters, a mechanism of forgetting and updating is proposed. A functional block diagram of the hidden Markov models adaptation algorithm is also provided with obtained results, which improve the efficiency of emotional speech recognition.

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The authors declare that there are no conflicts of interest present.