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The paper proposes a novel framework (CDDTLDA) using transfer learning and data augmentation to improve Chinese dialects discrimination under low-resource conditions, achieving state-of-the-art results on two benchmark corpora.
This paper investigates speech-driven features for fine-grained discrimination among Chinese dialects, using an end-to-end model that combines MFCC-based features with word-level embeddings via a CNN, outperforming text-driven methods.
Dolphin-CN-Dialect is a streaming-capable ASR model that improves dialect recognition through temperature-based sampling and redesigned tokenization, achieving competitive performance with a smaller model size.