Text conditioning and statistical language modeling for Romanian language

Domokos, József; Toderean, Gavril; Buza, Ovidiu

doi:10.1109/sped.2009.5156184

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The authors maintain that they performed partial validation of their resource at syllabification level. NaviRo (Domokos, Buza, and Toderean 2012) consists of more than 100,000 words extracted from Dexonline and transcribed in their phonemic form using an artificial neural network-based method starting from a seed set of manually transcribed entries. The authors report that they performed a manual check of the final dataset, but also mention that errors can still be found.…”

Section: The Romanian Language and Romanian Lexical Datasetsmentioning

confidence: 99%

RoLEX: The development of an extended Romanian lexical dataset and its evaluation at predicting concurrent lexical information

et al. 2022

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In this article, we introduce an extended, freely available resource for the Romanian language, named RoLEX. The dataset was developed mainly for speech processing applications, yet its applicability extends beyond this domain. RoLEX includes over 330,000 curated entries with information regarding lemma, morphosyntactic description, syllabification, lexical stress and phonemic transcription. The process of selecting the list of word entries and semi-automatically annotating the complete lexical information associated with each of the entries is thoroughly described. The dataset’s inherent knowledge is then evaluated in a task of concurrent prediction of syllabification, lexical stress marking and phonemic transcription. The evaluation looked into several dataset design factors, such as the minimum viable number of entries for correct prediction, the optimisation of the minimum number of required entries through expert selection and the augmentation of the input with morphosyntactic information, as well as the influence of each task in the overall accuracy. The best results were obtained when the orthographic form of the entries was augmented with the complete morphosyntactic tags. A word error rate of 3.08% and a character error rate of 1.08% were obtained this way. We show that using a carefully selected subset of entries for training can result in a similar performance to the performance obtained by a larger set of randomly selected entries (twice as many). In terms of prediction complexity, the lexical stress marking posed most problems and accounts for around 60% of the errors in the predicted sequence.

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Section: The Romanian Language and Romanian Lexical Datasetsmentioning

confidence: 99%