Analysis of Automatic Stress Assignment in Slovene

Marinčič, Domen; Tušar, Tea; Gams, Matjaž; Šef, Tomaž

doi:10.15388/informatica.2009.236

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ciobanu, Dinu, and Dinu [24] used SVM to find the boundary between syllables and predict stressed syllables for Romanian. Lorincz et al [25] describe "RoLEX"-a dataset for the Romanian language containing over 330,000 entries with information on the lemma, morphosyntactic description, syllabification, lexical stress, and phonemic transcription. Moreover, Marinčič, Tušar, Gams and Šef [26] used classification trees to determine the lexical stress position and type of accent in Slovenian.…”

Section: Related Workmentioning

confidence: 99%

System for Automatic Assignment of Lexical Stress in Croatian

Preradović

Prskalo

2022

Electronics

View full text Add to dashboard Cite

It is very popular today to integrate voice interfaces into IoT devices. The pronunciation and proper prosody of speech play a major role in the intelligibility and naturalness of synthesized voices. Each language has its own prosodic characteristics. In this paper, we present the results of a study aimed at testing the applicability of methods for modelling and predicting the prosodic features of the Croatian language. The extent to which their performance can be improved by incorporating linguistic features and linguistic peculiarities specific to the Croatian language was investigated. In the model learning process, tree classification was used to predict the lexical stress position and the type of stress in a word, and a lexicon of 1,011,785 word forms was used as the model learning set. Separate models were created for predicting the position and type of lexical stress. The results improved significantly after the rules for atonic words (clitics) were applied. A hybrid approach combining a rule-based approach and a modelling approach was also proposed. The final accuracy of assigning lexical stress using the hybrid approach was 95.3%.

show abstract

Section: Related Workmentioning

confidence: 99%