A Data2Vec2 speech encoder pre-trained on 300,000 hours of unlabeled Chinese dialect speech, connected to a small Qwen LLM via a linear projector and fine-tuned in four stages, sets a new state of the art on Chinese dialect ASR, including Kespeech at 6.48 percent CER.
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Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis
A Data2Vec2 speech encoder pre-trained on 300,000 hours of unlabeled Chinese dialect speech, connected to a small Qwen LLM via a linear projector and fine-tuned in four stages, sets a new state of the art on Chinese dialect ASR, including Kespeech at 6.48 percent CER.