{"title":"Research on word stress in Iranian languages by Soviet and Russian scholars","authors":"V. B. Ivanov, L.G. Silanteva","doi":"10.22363/2687-0088-31015","DOIUrl":null,"url":null,"abstract":"In recent years, considerable material has been accumulated in the field of experimental studies of Iranian languages, including the works by Soviet and Russian scholars, enabling us to make new generalizations regarding the acoustic characteristics of word stress as part of the problem of speech recognition. The study of Iranian languages has been rather uneven: most of the acoustic studies focused on Persian, and only a few covered other 11 languages described in this article. In addition, most of these studies have been published in Russian and therefore remain unknown to the wide international linguistic community. The purpose of the article is to sum up the achievements of Soviet and Russian scholars regarding the acoustic properties of the stressed syllable in Iranian languages. Different views of Soviet, Russian and foreign authors were compared. A number of positions with weak points in reasoning were screened out, and the most well-reasoned ones adopted as the most probable traits of word stress in Iranian languages. Tonal stress was found in Mazandarani, Persian and Tajik; quantitative - in Dari (Afghanistan), Sarikoli and theoretically in Rushani; multicomponental - in Abyanei, Gavruni, Gilaki, Pashto, and Wakhi. Ossetic is likely to have quantitative stress, but statistical proof hasn’t been found yet. Apparently, the overall situation reveals that tonal and quantitative stress types are typical for many Iranian languages. Dynamic stress is found in several languages, but only as a part of multicomponental one; and spectral stress is the rarest feature. The results achieved could be used in automated transcription and speech recognition services.","PeriodicalId":53426,"journal":{"name":"Russian Journal of Linguistics","volume":"2 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Linguistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22363/2687-0088-31015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, considerable material has been accumulated in the field of experimental studies of Iranian languages, including the works by Soviet and Russian scholars, enabling us to make new generalizations regarding the acoustic characteristics of word stress as part of the problem of speech recognition. The study of Iranian languages has been rather uneven: most of the acoustic studies focused on Persian, and only a few covered other 11 languages described in this article. In addition, most of these studies have been published in Russian and therefore remain unknown to the wide international linguistic community. The purpose of the article is to sum up the achievements of Soviet and Russian scholars regarding the acoustic properties of the stressed syllable in Iranian languages. Different views of Soviet, Russian and foreign authors were compared. A number of positions with weak points in reasoning were screened out, and the most well-reasoned ones adopted as the most probable traits of word stress in Iranian languages. Tonal stress was found in Mazandarani, Persian and Tajik; quantitative - in Dari (Afghanistan), Sarikoli and theoretically in Rushani; multicomponental - in Abyanei, Gavruni, Gilaki, Pashto, and Wakhi. Ossetic is likely to have quantitative stress, but statistical proof hasn’t been found yet. Apparently, the overall situation reveals that tonal and quantitative stress types are typical for many Iranian languages. Dynamic stress is found in several languages, but only as a part of multicomponental one; and spectral stress is the rarest feature. The results achieved could be used in automated transcription and speech recognition services.