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Transliteration

Transliteration is a mechanism for converting a word in a source (foreign) language to a target language, and often adopts approaches from machine translation. In machine translation, the objective is to preserve the semantic meaning of the utterance as much as possible while following the syntactic structure in the target language. In Transliteration, the objective is to preserve the original pronunciation of the source word as much as possible while following the phonological structures of the target language.

For example, the city’s name “Manchester” has become well known by people of languages other than English. These new words are often named entities that are important in cross-lingual information retrieval, information extraction, machine translation, and often present out-of-vocabulary challenges to spoken language technologies such as automatic speech recognition, spoken keyword search, and text-to-speech.

Source: Phonology-Augmented Statistical Framework for Machine Transliteration using Limited Linguistic Resources

Papers

Showing 91–100 of 435 papers

TitleStatusHype
Automatic word stress annotation of Russian unrestricted text—0
Character-Level Bangla Text-to-IPA Transcription Using Transformer Architecture with Sequence Alignment—0
Charles Translator: A Machine Translation System between Ukrainian and Czech—0
Classifying Arab Names Geographically—0
CLeLfPC: a Large Open Multi-Speaker Corpus of French Cued Speech—0
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints—0
Code-Mixed to Monolingual Translation Framework—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction—0
A Neural Network Transliteration Model in Low Resource Settings—0
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