Abstract
Taiwanese Hokkien (Taigi) presents unique opportunities for advancing speech technology methodologies that can generalize to diverse linguistic contexts. We introduce Breeze Taigi, a comprehensive framework centered on standardized benchmarks for evaluating Taigi speech recognition and synthesis systems. Our primary contribution is a reproducible evaluation methodology that leverages parallel Taiwanese Mandarin resources. We provide 30 carefully curated Mandarin-Taigi audio pairs from Taiwan's Executive Yuan public service announcements with normalized ground truth transcriptions. We establish Character Error Rate (CER) as the standard metric and implement normalization procedures to enable fair cross-system comparisons. To demonstrate the benchmark's utility and provide reference implementations, we develop speech recognition and synthesis models through a methodology that leverages existing Taiwanese Mandarin resources and large-scale synthetic data generation. In particular, we fine-tune a Whisper model on approximately 10,000 hours of Taigi synthetic speech data. Our ASR model achieves 30.13% average CER on the benchmark, outperforming existing commercial and research systems. By providing standardized evaluation protocols, diverse training datasets, and open baseline models, we offer a replicable framework with methodologies applicable to various linguistic contexts.
Abstract (translated)
台语（Taigi）为推进可泛化至多元语言场景的语音技术方法论提供了独特机遇。本文提出Breeze Taigi框架，该框架以标准化基准测试为核心，用于评估台语语音识别与合成系统。我们的核心贡献是建立了一套可复现的评估方法论，充分利用平行台语-普通话资源。我们提供30对经精心筛选的普通话-台语音频样本，源自台湾行政院公共事务 announcements，并配备标准化真值转写。我们确立字错误率（CER）作为标准评估指标，并实施标准化流程以实现跨系统公平对比。为验证基准实用性并提供参考实现，我们通过利用现有台语-普通话资源及大规模合成数据生成的方法论，开发了语音识别与合成模型。特别地，我们基于约10,000小时台语合成语音数据对Whisper模型进行了微调。我们的ASR模型在基准测试中取得30.13%的平均CER，性能超越现有商业及研究系统。通过提供标准化评估协议、多样化训练数据集及开源基线模型，我们构建了一套可复现的框架，其方法论适用于各类语言场景。
URL
https://arxiv.org/abs/2603.19259
