izmertechИзмерительная техникаIzmeritel`naya Tekhnika0368-10252949-5237ФГУП "ВНИИФТРИ"10.32446/10.32446/0368-1025it.2025-3-67-78izmertech-2282Research ArticleАКУСТИЧЕСКИЕ ИЗМЕРЕНИЯACOUSTIC MEASUREMENTSМетод кодирования голосового источника речи со сжатием данных на основе модели линейного предсказанияMethod of voice source coding with data compression based on the linear prediction modelhttps://orcid.org/0000-0003-3045-3337СавченкоВ. В.SavchenkoV. V.

Владимир Васильевич Савченко, доктор технических наук, профессор

Нижний Новгород

Vladimir V. Savchenko

Nizhny Novgorod

vvsavchenko@yandex.ruhttps://orcid.org/0000-0002-2776-5471СавченкоЛ. В.SavchenkoL. V.

Людмила Васильевна Савченко, кандидат технических наук, доцент

Нижний Новгород

Lyudmila V. Savchenko

Nizhny Novgorod

vvsavchenko@yandex.ruНациональный исследовательский университет «Высшая школа экономики»РоссияNational Research University Higher School of EconomicsRussian Federation2025140720257436778Copyright © ФГУП "ВНИИФТРИ", 20252025ФГУП "ВНИИФТРИ"ФГУП "ВНИИФТРИ"https://www.izmt.ru/jour/about/submissions#copyrightNoticehttps://www.izmt.ru/jour/article/view/2282

В рамках динамично развивающегося направления исследований в области акустических измерений рассмотрена задача кодирования голоса диктора со сжатием данных на основе модели линейного предсказания. С использованием критерия минимума средней мощности голосового источника в процессе речеобразования задача сведена к кодированию в режиме реального времени сигнала ошибки линейного предсказания. Разработан новый метод голосового кодирования: с клиппированием ошибки линейного предсказания, который не связан с многозатратными в вычислительном отношении процедурами измерений начальной фазы и частоты основного тона речевого сигнала. Рассмотрен пример его технической реализации в режиме мягкого реального времени. Поставлен и проведен натурный эксперимент, в ходе которого исследовалась эффективность разработанного метода в сопоставлении с эффективностью его наиболее известного из аналогов: метода дискретного косинусного преобразования. Показано, что за счет ослабления артефактов сжатия данных в восстановленном речевом сигнале предложенный метод характеризуется выигрышем в полтора-два раза по точности кодирования голосового источника и при этом не требует детектирования гласных звуков речи и пауз в речевом сигнале. Полученные результаты могут быть использованы при разработке новых и модернизации существующих систем и алгоритмов в области автоматической обработки и синтеза речи, мобильной речевой связи, искусственного интеллекта и других приложений речевых технологий со сжатием данных на основе модели линейного предсказания.

Within the framework of a dynamically developing direction of research in the field of acoustic measurements – analysis and evaluation of parameters of the excitation signal of acoustic oscillations in the vocal tract of a speaker – the problem of coding a voice source of speech with data compression based on a linear prediction model is considered. Using the criterion of minimum average the voice source power in the speech production process, the problem is reduced to real-time coding of the linear prediction error signal. A new method of voice coding has been developed: with clipping of the linear prediction error, which is not associated with computationally expensive procedures for measuring the initial phase and frequency of the fundamental tone of the speech signal. An example of its technical implementation in soft real-time mode is considered. A full-scale experiment was set up and carried out, during which a comparative analysis of the effectiveness of the proposed method and the widely used discrete cosine transform method was performed. It is shown that due to the weakening of data compression artifacts in the reconstructed speech signal, the accuracy of coding the voice source using the developed method is one and a half to two times higher, and there is no need to detect vowel sounds of speech and pauses in the speech signal. The obtained results will be useful in the development of new and modernization of existing systems and algorithms in the fields of automatic speech processing and synthesis, mobile speech communication, artificial intelligence and other applications of speech technologies with data compression based on the linear prediction model.

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