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

arXiv:2603.19259v1 Announce Type: cross 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-scal

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Yu-Siang Lan, Chia-Sheng Liu, Yi-Chang Chen, Po-Chun Hsu, Allyson Chiu, Shun-Wen Lin, Da-shan Shiu, Yuan-Fu Liao · · 1 min read · 17 views
#cs.CL #cs.AI

arXiv:2603.19259v1 Announce Type: cross 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.

Executive Summary

This article presents Breeze Taigi, a comprehensive framework for evaluating Taiwanese Hokkien speech recognition and synthesis systems. The authors develop a standardized benchmark, leveraging parallel Taiwanese Mandarin resources, and provide a reproducible evaluation methodology. They establish Character Error Rate as the standard metric and implement normalization procedures to enable fair cross-system comparisons. The authors also develop speech recognition and synthesis models through a methodology that leverages existing Taiwanese Mandarin resources and large-scale synthetic data generation. Their ASR model achieves 30.13% average CER on the benchmark, outperforming existing commercial and research systems. This framework offers a replicable methodology with applicability to various linguistic contexts, advancing speech technology for diverse languages.

Key Points

  • ▸ Develops a standardized benchmark for evaluating Taiwanese Hokkien speech recognition and synthesis systems
  • ▸ Leverages parallel Taiwanese Mandarin resources for reproducible evaluation methodology
  • ▸ Establishes Character Error Rate as the standard metric and implements normalization procedures for fair cross-system comparisons

Merits

Innovative Framework

The authors develop a comprehensive framework that addresses the unique challenges of Taiwanese Hokkien speech recognition and synthesis, offering a replicable methodology with broad applicability to various linguistic contexts.

High-Quality Evaluation Metrics

The authors establish Character Error Rate as the standard metric and implement normalization procedures, enabling fair cross-system comparisons and enhancing the reliability of their evaluation results.

Demerits

Limited Dataset

The authors rely on a relatively small dataset of 30 carefully curated Mandarin-Taigi audio pairs, which may limit the generalizability of their results and the robustness of their framework in more diverse linguistic contexts.

Dependence on Mandarin Resources

The authors' methodology relies heavily on existing Taiwanese Mandarin resources, which may introduce bias and limit the applicability of their framework to other languages.

Expert Commentary

This article presents a significant contribution to the field of speech technology, as it addresses the unique challenges of Taiwanese Hokkien speech recognition and synthesis. The authors' framework offers a replicable methodology with broad applicability to various linguistic contexts, advancing our understanding of the intersection of language and technology. However, the limited dataset and dependence on Mandarin resources necessitate caution in interpreting the results and the robustness of the framework. Nevertheless, this study highlights the importance of standardized evaluation protocols and high-quality evaluation metrics in speech technology, underscoring the need for more research on speech recognition and synthesis for underrepresented languages.

Recommendations

  • ✓ Future research should focus on expanding the dataset and exploring alternative methodologies that minimize dependence on Mandarin resources.
  • ✓ The Breeze Taigi framework should be further validated and tested with diverse linguistic data to ensure its robustness and generalizability across languages and domains.

Sources

Original: arXiv - cs.AI

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