Leveraging Alignment and Phonology for low-resource Indic to English Neural Machine Transliteration
In this paper we present a novel transliteration technique based on Orthographic Syllable(OS) segmentation for low-resource Indian languages (ILs). Given that alignment has produced promising results in Statistical Machine Transliteration systems and phonology plays an important role in transliteration, we introduce a new model which uses alignment representation similar to that of IBM model 3 to pre-process the tokenized input sequence and then use pre-trained source and target OS-embeddings for training. We apply our model for transliteration from ILs to English and report our accuracy based on Top-1 Exact Match. We also compare our accuracy with a previously proposed Phrase-Based model and report improvements.
Code (0)
등록된 구현이 없습니다.
Tasks
TransliterationSimilar Papers 제목 키워드 기반
Improve Bilingual TTS Using Dynamic Language and Phonology Embedding
In most cases, bilingual TTS needs to handle three types of input scripts: first language only, second language only, and second language embedded in the first language. In the latter two situations, the pronunciation an…
Phonology-Augmented Statistical Framework for Machine Transliteration using Limited Linguistic Resources
Transliteration converts words in a source language (e.g., English) into words in a target language (e.g., Vietnamese). This conversion considers the phonological structure of the target language, as the transliterated o…
TransliterationExploiting machine algorithms in vocalic quantification of African English corpora
Towards procedural fidelity in the processing of African English speech corpora, this work demonstrates how the adaptation of machine-assisted segmentation of phonemes and automatic extraction of acoustic values can sign…
Segmentation