Exploring Transliteration-Based Zero-Shot Transfer for Amharic ASR

The performance of Automatic Speech Recognition (ASR) depends on the availability of transcribed speech datasets-often scarce or non-existent for many of the world's languages.This study investigates alternative strategies to bridge the data gap using zeroshot cross-lingual transfer, leveraging transliteration as a method to utilize data from other languages.We experiment with transliteration from various source languages and demonstrate ASR performance in a low-resourced language, Amharic.We find that source data that align with the character distribution of the test data achieve the best performance, regardless of language family.We also experiment with fine-tuning with minimal transcribed data in the target language.Our findings demonstrate that transliteration, particularly when combined with a strategic choice of source languages, is a viable approach for improving ASR in zeroshot and low-resourced settings.