Towards Turkish ASR: Anatomy of a rule-based Turkish g2p
Duygu Altinok
TL;DR
This paper presents a rule-based Turkish grapheme-to-phoneme converter that uses morphological analysis to produce phonetic transcriptions with stress positions, implemented in Python for improved Turkish ASR.
Contribution
It introduces a novel rule-based G2P system tailored for Turkish, integrating morphological analysis and stress placement, advancing Turkish ASR development.
Findings
Accurate phoneme conversion aligned with Turkish morphology
Effective stress position prediction in pronunciations
Python implementation facilitates integration and testing
Abstract
This paper describes the architecture and implementation of a rule-based grapheme to phoneme converter for Turkish. The system accepts surface form as input, outputs SAMPA mapping of the all parallel pronounciations according to the morphological analysis together with stress positions. The system has been implemented in Python
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Taxonomy
TopicsNatural Language Processing Techniques · Speech and dialogue systems · Multi-Agent Systems and Negotiation