Sinhala G2P Conversion for Speech Processing

Nadungodage, Thilini; Liyanage, Chamila; Prerera, Amathri; Pushpananda, Randil; Weerasinghe, Ruvan

doi:10.21437/sltu.2018-24

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…2) Lexicon: The lexicon plays a crucial role in the pronunciation model of a statistical ASR system, as it maps words to their corresponding spoken phone sequences [3]. In this study, the lexicon was created using two tools: "Sinhala G2P Conversion" [15] and "Subasa Transliterator". These tools were employed to generate the necessary mappings between words and their respective phonetic representations.…”

Section: B Data Preparationmentioning

confidence: 99%

Applicability of End-to-End Deep Neural Architecture to Sinhala Speech Recognition

Gamage,

Pushpananda,

Nadungodage

et al. 2024

Int J on Adv. in ICT for Emerging Countries

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This research presents a study on the application of end-to-end deep learning models for Automatic Speech Recognition in the Sinhala language, which is characterized by its high inflection and limited resources.We explore two e2e architectures, namely the e2e Lattice-Free Maximum Mutual Information model and the Recurrent Neural Network model, using a restricted dataset. Statistical models with 40 hours of training data are established as baselines for evaluation. Our pretrained endto-end Automatic Speech Recognition models achieved a Word Error Rate of 23.38% by far the best word-error-rate achieved for low resourced Sinhala Language. Our models demonstrate greater contextual independence and faster processing, making them more suitable for general-purpose speech-to-text translation in Sinhala.

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Section: B Data Preparationmentioning

confidence: 99%

Applicability of End-to-End Deep Neural Architecture to Sinhala Speech Recognition

Gamage,

Pushpananda,

Nadungodage

et al. 2024

Int J on Adv. in ICT for Emerging Countries

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“…Based on the earlier work by Weerasinghe et al [155], Wasala et al [156] have developed methods for Sinhala grapheme-to-phoneme conversion along with a set of rules for schwa epenthesis. This work was then extended by Nadungodage et al [157]. Weerasinghe et al [158] developed a Sinhala text-to-speech system.…”

Section: Phonological Toolsmentioning

confidence: 99%

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Silva¹

2019

Preprint

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Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field.

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Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments

Kahawanugoda

Gnanarathna

Meegoda

et al. 2022

2022 4th International Conference on Advancements in Computing (ICAC)

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Sinhala G2P Conversion for Speech Processing

Cited by 6 publications

References 5 publications

Applicability of End-to-End Deep Neural Architecture to Sinhala Speech Recognition

Applicability of End-to-End Deep Neural Architecture to Sinhala Speech Recognition

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments

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