WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem
Abstract
Speech processing for low-resource dialects remains a fundamental challenge in developing inclusive and robust speech technologies. Despite its linguistic significance and large speaker population, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models. In this work, we present WenetSpeech-Wu, the first large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect, comprising approximately 8,000 hours of diverse speech data. Building upon this dataset, we introduce WenetSpeech-Wu-Bench, the first standardized and publicly accessible benchmark for systematic evaluation of Wu dialect speech processing, covering automatic speech recognition (ASR), Wu-to-Mandarin translation, speaker attribute prediction, speech emotion recognition, text-to-speech (TTS) synthesis, and instruction-following TTS (instruct TTS). Furthermore, we release a suite of strong open-source models trained on WenetSpeech-Wu, establishing competitive performance across multiple tasks and empirically validating the effectiveness of the proposed dataset. Together, these contributions lay the foundation for a comprehensive Wu dialect speech processing ecosystem, and we open-source proposed datasets, benchmarks, and models to support future research on dialectal speech intelligence.
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Research summary
The paper presents WenetSpeech-Wu, a pioneering effort to create a comprehensive speech processing ecosystem for the Wu dialect, addressing critical gaps in data, benchmarks, and models. The technical contributions are substantial, with a well-defined methodology and rigorous experimental validation, making it a significant advancement in the field of speech processing for low-resource dialects.
Methodology
The methodology employed in this paper is robust, focusing on the creation of a large-scale, multi-dimensionally annotated speech corpus for the Wu dialect. The authors detail a comprehensive data collection and annotation pipeline that integrates various automated and semi-automated techniques to ensure high-quality outputs. The use of advanced models for automatic transcription and emotion recognition, along with a structured quality grading system, reflects a thorough approach to addressing the challenges of low-resource dialect speech processing.
Results & evaluation
The experiments conducted are extensive and well-structured, covering a variety of speech processing tasks including ASR, TTS, and emotion recognition. The introduction of the WenetSpeech-Wu-Bench as a standardized benchmark is particularly noteworthy, as it facilitates fair evaluation and comparison across different models. The performance metrics reported demonstrate significant improvements over existing systems, validating the effectiveness of the proposed models and dataset.
Reproducibility
The paper provides sufficient details regarding the data collection, annotation processes, and model training procedures, which enhances reproducibility. The availability of the dataset and models on GitHub further supports the research community in replicating and building upon this work. However, the reliance on automated annotations may introduce some variability that could affect reproducibility in practical applications.
Limitations
While the dataset is extensive, the authors acknowledge that the distribution across dialects and domains is not perfectly balanced, which could impact model generalization. Additionally, the automated annotation processes, despite quality controls, may still contain noise, potentially affecting the reliability of certain tasks.
Broader impact
This work has significant implications for the development of inclusive speech technologies, particularly for underrepresented dialects. By establishing a comprehensive ecosystem for Wu dialect speech processing, the authors pave the way for future research and applications in low-resource language settings, potentially enhancing accessibility and usability in various domains. The paper presents WenetSpeech-Wu, a pioneering effort to create a comprehensive speech processing ecosystem for the Wu dialect, addressing critical gaps in data, benchmarks, and models. The technical contributions are substantial, with a well-defined methodology and rigorous experimental validation, making it a significant advancement in the field of speech processing for low-resource dialects.
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