Performance Comparison of Pre-trained Models for Speech-to-Text in Turkish: Whisper-Small and Wav2Vec2-XLS-R-300M
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Abstract (arXiv)
In this study, the performances of the Whisper-Small and Wav2Vec2-XLS-R-300M models which are two pre-trained multilingual models for speech to text were examined for the Turkish language. Mozilla Common Voice version 11.0 which is prepared in Turkish language and is an open-source data set, was used in the study. The multilingual models, Whisper- Small and Wav2Vec2-XLS-R-300M were fine-tuned with this data set which contains a small amount of data. The speech to text performance of the two models was compared. WER values are calculated as 0.28 and 0.16 for the Wav2Vec2-XLS- R-300M and the Whisper-Small models respectively. In addition, the performances of the models were examined with the test data prepared with call center records that were not included in the training and validation dataset.
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References · 8
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 589
- 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 496
- 2015Attention-Based Models for Speech Recognitioncited by 400
- 2020A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Taskscited by 51
- 2019A Comparative Study on End-to-end Speech to Text Translationcited by 40
- 2022Efficient Speech Translation with Pre-trained Modelscited by 1
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