加纳语言青少年健康沟通中自动语音识别（ASR）的基准测试与领域自适应
Benchmarking and Domain Adaptation of Automatic Speech Recognition (ASR) for Adolescent Health Communication in Ghanaian Languages
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中文总结 AI 辅助
本研究对三种加纳语言的ASR系统进行基准测试和领域自适应，通过微调Qwen3-ASR-0.6B显著降低WER，并部署KasaHealth应用验证，表明领域数据是关键制约因素。
中文摘要 AI 辅助
本文对三种加纳语言（Twi、Dagbani和Ewe）的青少年健康沟通中的自动语音识别（ASR）进行了端到端研究。工作分三个相互关联的阶段进行：首先，我们在通用领域的圣经语料库和青少年性与生殖健康（ASRH）领域ASR数据集上，使用字符错误率和词错误率（CER、WER）对五个ASR系统（三个特定语言的Wav2Vec2模型和两个多模态大语言模型Gemma 3n与Gemma 4）进行基准测试。其次，在基准测试的指导下，我们进行有监督的领域自适应：尽管Gemma 4是最强的零样本候选，但对其微调在计算上不可行，因此我们转向紧凑的Qwen3-ASR-0.6B，在大型加纳圣经语料库（约9万样本）上微调，并严格在留出的、人工收集的领域内音频上评估。微调降低了每种语言的WER，其中Ewe语最为显著（WER从109.3%降至64.8%，下降44.5个百分点；CER从65.1%降至24.9%）。第三，我们通过KasaHealth验证了这项工作，这是一个以语音为先的实时ASRH应用，部署在所有三种语言中，并辅以Senti-Check技术评估工具。KasaHealth接受了50名社区受访者的测试，实现了100%的聊天认可率、72%的良好或优秀翻译评分以及92%的推荐率，同时揭示了最制约实际应用的领域差距。在三个阶段中，证据汇聚于一点：对于这些语言，制约因素是经过验证的领域内数据，而非模型能力或计算资源。
英文摘要
This paper presents an end-to-end study of automatic speech recognition (ASR) for adolescent health communication in three Ghanaian languages (Twi, Dagbani, and Ewe). The work proceeds in three connected stages; First, we benchmark five ASR systems (three language-specific Wav2Vec2 models and two multimodal LLMs, Gemma 3n and Gemma 4) on a general-domain Bible corpus and a Youth Adolescent Sexual and Reproductive Health (ASRH) Domain ASR dataset, using Character and Word Error Rate (CER, WER). Second, guided by the benchmark, we perform supervised domain adaptation: although Gemma 4 was the strongest zero-shot candidate, fine-tuning it proved computationally infeasible, so we pivoted to the compact Qwen3-ASR-0.6B, fine-tuned on a large Ghana Bible corpus (~90k samples) and evaluated strictly on held-out human-collected in-domain audio. Fine-tuning reduced WER on every language, most dramatically for Ewe (WER from 109.3% to 64.8%, a drop of 44.5 pp; CER from 65.1% to 24.9%). Third, we validate the work through KasaHealth, a live voice-first ASRH application deployed in all three languages, complemented by Senti-Check, a technical evaluation harness. KasaHealth was tested by 50 community respondents and achieved a 100% chat-approval rate, a 72% Good-or-Excellent translation rating, and a 92% would-recommend rate, while surfacing the domain gaps that most constrain real-world use. Across all three stages the evidence converges: for these languages the binding constraint is validated in-domain data, not model capability or computation.
发表机构
- University of Cape Coast（海岸角大学）
- Ghana Natural Language Processing（加纳自然语言处理机构）
机构由 AI 辅助整理，请以论文原文为准。
