Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation
Abstract
Despite rapid advances in automatic speech recognition (ASR) and large audio-language models, robust recognition in real-world environments remains limited by an "acoustic robustness bottleneck": models often lose acoustic grounding and produce omissions or hallucinations under severe, compositional distortions. We propose Mega-ASR, a unified ASR-in-the-wild framework that combines scalable compound-data construction with progressive acoustic-to-semantic optimization. We introduce Voices-in-the-Wild-2M, covering 7 classic acoustic phenomena and 54 physically plausible compound scenarios, and train Mega-ASR with Acoustic-to-Semantic Progressive Supervised Fine-Tuning and Dual-Granularity WER-Gated Policy Optimization. Extensive experiments demonstrate that Mega-ASR achieves significant advantages over prior state-of-the-art systems on adverse-condition ASR benchmarks (45.69% vs. 54.01% on VOiCES R4-B-F, and 21.49% vs. 29.34% on NOIZEUS Sta-0). On complex compositional acoustic scenarios, Mega-ASR further delivers over 30% relative WER reduction against strong open- and closed-source baselines, establishing a scalable paradigm for robust ASR in-the-wild.
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Research summary
The paper presents Mega-ASR, a unified framework for robust ASR in challenging acoustic environments, significantly advancing the state of the art in the field. The innovative dataset construction, novel training methodologies, and comprehensive evaluation underscore its potential impact on real-world applications of speech recognition technology.
Methodology
The methodology presented in this paper is robust and innovative, focusing on the construction of a large-scale dataset (Voices-in-the-Wild-2M) that simulates various real-world acoustic phenomena. The authors employ a hierarchical simulation pipeline that effectively combines primitive acoustic effects to create atomic and compound scenarios, addressing the limitations of existing ASR datasets. The use of Acoustic-to-Semantic Progressive Supervised Fine-Tuning and Dual-Granularity WER-Gated Policy Optimization is particularly noteworthy, as it reflects a sophisticated understanding of the challenges faced in ASR under adverse conditions. The progressive fine-tuning approach is well-justified, and the dual-granularity reward mechanism is a novel contribution that enhances the model's ability to recover semantic information.
Results & evaluation
The experimental evaluation is comprehensive, with extensive benchmarks against both open-source and closed-source ASR models. The authors provide detailed comparisons across various datasets, demonstrating significant improvements in word error rates (WER) under challenging conditions. The results are well-presented, with clear metrics that highlight the advantages of Mega-ASR over existing models. The inclusion of qualitative case studies further enriches the evaluation by illustrating the types of errors mitigated by the proposed approach.
Reproducibility
The paper provides sufficient implementation details, including training setups, hyperparameters, and data sources, which enhances reproducibility. The authors also share their dataset and evaluation benchmarks, facilitating further research in this area. However, the complexity of the model and the specific tuning of parameters may pose challenges for complete replication without access to the original code.
Limitations
One limitation of the study is the potential overfitting to the simulated conditions, as real-world scenarios can be more diverse and unpredictable. Additionally, while the dataset is extensive, it may not cover all possible acoustic phenomena encountered in various environments. The reliance on simulated data may also limit the model's performance when faced with novel, unseen conditions.
Broader impact
The implications of this research are significant, as it addresses a critical bottleneck in ASR technology, particularly for applications in real-world environments. The advancements in robustness and semantic recovery could enhance the usability of ASR systems in various fields, including telecommunications, accessibility for the hearing impaired, and human-computer interaction. The proposed framework could serve as a foundation for future research in robust ASR and related areas. The paper presents Mega-ASR, a unified framework for robust ASR in challenging acoustic environments, significantly advancing the state of the art in the field. The innovative dataset construction, novel training methodologies, and comprehensive evaluation underscore its potential impact on real-world applications of speech recognition technology.
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