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arXiv · 2501.17615

Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition

Zhengdong Yang·Qianying Liu·Sheng Li·Fei Cheng·Chenhui Chu
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Abstract

We present a novel approach centered on the decoding stage of Automatic Speech Recognition (ASR) that enhances multilingual performance, especially for low-resource languages. It utilizes a cross-lingual embedding clustering method to construct a hierarchical Softmax (H-Softmax) decoder, which enables similar tokens across different languages to share similar decoder representations. It addresses the limitations of the previous Huffman-based H-Softmax method, which relied on shallow features in token similarity assessments. Through experiments on a downsampled dataset of 15 languages, we demonstrate the effectiveness of our approach in improving low-resource multilingual ASR accuracy.

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Zhengdong Yang, Qianying Liu, Sheng Li, Fei Cheng, Chenhui Chu. 2025-01-29. Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition. https://doi.org/10.1109/taslpro.2025.3617233

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