Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings

Nosek, Tijana; Suzic, Sinisa; Pekar, Darko; Obradovic, Radovan; Secujski, Milan; Delić, Vlado

doi:10.9781/ijimai.2021.11.005

Cited by 6 publications

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“…The word embedding approach is mostly used to disambiguate the meaning of words including acronym (Charbonnier & Wartena, 2018). For example, Multi sense embeddings are done to disambiguate the meaning of words (Prasad Mudigonda & Sharma, 2020), and speech synthesis is also done based on multiple embeddings (Nosek et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

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Heuristics‐based sequence labelling model for finding educational domain acronym expansions

Menaha¹,

Jayanthi

2022

Expert Systems

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An acronym is a short form of the text used in many applications like Web search, Research and scientific documents, information retrieval, biomedical documents, etc.The main objective of the work is to develop a heuristics-based sequence labelling model to identify acronym expansion pairs in educational documents. The proposed system is implemented in four phases: (i) Data Collection (ii) Acronym Sequence Identification (iii) Expansion Determination and (iv) Acronym Expansion Validation. In this work, the acronym sequences identification is done by heuristic rules. Determining expansions is accomplished by a heuristics-based sequence labelling model. Again, the validation of acronym expansion sequences is also done by using heuristics rules.The result section shows that our proposed heuristics-based sequence labelling model achieved 98.4% accuracy in finding acronym expansion pairs from education research documents. The results are also compared with the acronym finder online acronym expansion repository and proved that more new acronym expansion sequences are identified by using our model.

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Section: Related Workmentioning

confidence: 99%

“…For example, Multi sense embeddings are done to disambiguate the meaning of words (Prasad Mudigonda & Sharma, 2020), and speech synthesis is also done based on multiple embeddings (Nosek et al, 2021).…”

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confidence: 99%