NOW LET US – AI RAG SaaS Studio TP.HCM
NOW LET US
Digital Product Studio
Back to news
AGENTIC-SYSTEMS...1 min read

Breeze Taigi: Benchmarks and Models for Taiwanese Hokkien Speech Recognition and Synthesis

Share
NOW LET US Article – Breeze Taigi: Benchmarks and Models for Taiwanese Hokkien Speech Recognition and Synthesis

Breeze Taigi introduces a comprehensive framework and standardized benchmarks for evaluating Taiwanese Hokkien speech systems, featuring a Whisper model fine-tuned on 10,000 hours of synthetic data.

Computer Science > Computation and Language

Title:Breeze Taigi: Benchmarks and Models for Taiwanese Hokkien Speech Recognition and Synthesis

View PDF HTML (experimental)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.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

© 2026 Now Let Us. All rights reserved.

Source: arXiv cs.AI Recent

Advertisement
Ad slot ready: 5887729102

More in this category

EXPLORE TOPICS

Discover All Categories

Deep dive into the specific technology sectors that matter most to you.