arXiv cs.CL· Oche David Ankeli, Hassan Adelani Luqman, Theodora Ngozi Egbunike, Joshua Gottschalk, Bonaventure F. P. Dossou·· 12 小时前AI 评分18
面向低资源医疗 ASR 的纯文本域适应：Hausa 语上的浅层融合与密度比重打分
Text-Only Domain Adaptation for Low-Resource Medical ASR: Shallow Fusion and Density Ratio Rescoring for Hausa
AI 导读
研究在固定 MMS-1B-FL102 声学模型上，用纯文本语言模型融合提升 Hausa 语医疗 ASR，浅层融合（SF）与密度比方法（DRA）将 WER 从 29.56% 分别降至 23.55% 和 23.33%，均显著优于贪心解码（p < 0.001），但两者差异不显著（p = 0.423）。
正文 · 原文
Abstract:Automatic speech recognition (ASR) systems often struggle with specialised medical vocabulary, especially in low-resource languages where domain-specific speech data are scarce. We investigate text-only domain adaptation for Hausa medical ASR using the fixed MMS-1B-FL102 acoustic model and two decoding-time language-model fusion methods: Shallow Fusion (SF) and the Density Ratio Approach (DRA). A 4-gram KenLM trained on 19,421 Hausa health sentences serves as the medical target model, while a 75,372-sentence general-domain model provides the source prior for DRA. Evaluation uses 250 recordings of 25 physician-reviewed prompts from 10 speakers across six northern Nigerian states. Under prompt-disjoint five-fold cross-validation, SF and DRA reduce WER from 29.56% to 23.55% and 23.33%, respectively. Both significantly outperform greedy decoding (p < 0.001), while their difference is not significant (p = 0.423). However, general-LM-only SF achieves 23.96% WER, indicating that the medical LM's additional improvement over a general LM is modest. Medical Term Error Rate also improves under fusion, but does not establish a distinct medical-domain advantage. These findings demonstrate that lightweight, text-only language-model fusion can improve Hausa medical ASR without additional domain-specific speech collection.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.05744 [cs.LG] |
| (or arXiv:2611.05744v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05744 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Oche Ankeli [view email]
[v1]
Tue, 3 Nov 2026 23:32:33 UTC (1,675 KB)
来源：arXiv cs.CL · arxiv.org