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arXiv · 2606.10360

ViP-VL: Vietnamese Self-supervised Speech Pretraining Model with Vector-Quantization Learning

Khanh Le·Kiet Anh Hoang·Bao Nguyen·Duy Vo·Dung Vo·Thai Tran·Linh Pham·Khoa D Doan
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Abstract

We present ViP-VL, an efficient Vietnamese Self-supervised speech Pretraining model leveraging Vector-quantization Learning. To bridge the gap between high-resolution audio and efficient processing, ViP-VL incorporates Acoustic Stacking and Receptive Field Alignment to enable a synchronized 8x subsampling rate within the ChunkFormer architecture, while further enhancing representation robustness through a specialized Mask Selection Strategy during pretraining on the BEST-RQ framework. Pretrained on 17,000 hours of unlabeled Vietnamese speech, our model establishes new state-of-the-art results across four major downstream tasks: Automatic Speech Recognition, Speech Emotion Recognition, Dialect Classification, and Speaker Verification. To facilitate future research and the development of high-performance Vietnamese speech technologies, we publicly release our pretrained weights and implementation at github.com/khanld/chunkformer.

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Khanh Le, Kiet Anh Hoang, Bao Nguyen, Duy Vo, Dung Vo, Thai Tran, Linh Pham, Khoa D Doan. 2026-06-09. ViP-VL: Vietnamese Self-supervised Speech Pretraining Model with Vector-Quantization Learning. https://arxiv.org/abs/2606.10360

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