Vietnamese end-to-end speech recognition using wav2vec 2.0
Our models are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. We use wav2vec2 architecture for the pre-trained model. For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. On the Vivos dataset, we achieved a WER score of 6.15
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Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Handwriting Recognitionspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
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