Comparative Analysis of State-of-the-Art Speech Recognition Models for Low-Resource Marathi Language


Authors : Suhas Waghmare; Chirag Brahme; Siddhi Panchal; Numaan Sayed; Mohit Goud

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/mt6jz3wy

Scribd : https://tinyurl.com/mr4cftne

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1816

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Abstract : In this research, we present a comparative analysis of two state-of-the-art speech recognition models, Whisper by OpenAI and XLSR Wave2vec by Facebook, applied to the low-resource Marathi language. Leveraging the Common Voice 16 dataset, we evaluated the performance of these models using the word error rate (WER) metric. Our findings reveal that the Whisper (Small) model achieved a WER of 45%, while the XLSR Wave2vec model obtained a WER of 71%. This study sheds light on the capabilities and limitations of current speech recognition technologies for low-resource languages and provides valuable insights for further research and development in this domain.

Keywords : Speech Recognition, State-of-the-Art Models, Whisper, XLSR Wave2vec, Marathi Language, Low-Resource.

In this research, we present a comparative analysis of two state-of-the-art speech recognition models, Whisper by OpenAI and XLSR Wave2vec by Facebook, applied to the low-resource Marathi language. Leveraging the Common Voice 16 dataset, we evaluated the performance of these models using the word error rate (WER) metric. Our findings reveal that the Whisper (Small) model achieved a WER of 45%, while the XLSR Wave2vec model obtained a WER of 71%. This study sheds light on the capabilities and limitations of current speech recognition technologies for low-resource languages and provides valuable insights for further research and development in this domain.

Keywords : Speech Recognition, State-of-the-Art Models, Whisper, XLSR Wave2vec, Marathi Language, Low-Resource.

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